Data Science Leadership

Build a Data Science Team from Scratch

Is your organization beginning it’s analytics or AI journey? One of the first steps will be building a data science team to help develop and deploy machine learning models. I was lucky to have the opportunity to be the first data scientist at a tech startup. In this article, I will share how we built a data science team from scratch.

Leadership Buy-in

The importance of executive sponsorship for your data science efforts cannot be overstated.

In my current role (not the tech startup), predictive modeling is core to our company’s value proposition. As a result, we have a large data science team relative to the size of the company and access to great compute resources.

But at the tech startup, predictive modeling was viewed by many as a nice-to-have. I made business cases to product managers for why they should add predictive components to their products. Most were interested, but early on none could find time on the roadmap given other priorities.

As the company and it’s product offerings expanded, eventually new senior leaders joined and key existing leaders began to believe data science was part of the critical path. From there, we finally got models in production and grew the team.

If your organization doesn’t have an executive sponsor, be persistent in building your business case but expect an uphill battle early on.

Infrastructure Prerequisites

Before data scientists can be productive, you need 1) data and 2) data engineers that can make it accessible. (Read this if you’re not sure about the difference between a data engineer and a data scientist.)

When I joined as the first data scientist, data was locked in a transactional database with prohibitively slow query times. An engineering team was in the middle of building a Spark-based data platform, but it was designed for customer reporting, not data science. So to get access to the data and do my work, I sat next to the data architect and learned Scala.

It wasn’t until the business intelligence team built a data warehouse in Snowflake when life as a data scientist got easy. None of my models made it to production until this data warehouse was built. Something worth noting here: the business intelligence team doubled in size to build and maintain the data warehouse.

Org Structure

There’s no easy answer to the question of where in your organization a data science team should live. Expect to go through some trial and error with your data science org structure in the early days. I personally had 7 different bosses in 2.5 years and went through 3 re-orgs.

Nonetheless, here are some popular org structures for data science.

  • In parallel with software engineers
    • If your data science team is building models for production, customer-facing systems, it probably should report into the CTO.
    • Data science is similar to DevOps in that it is separate from but in support of software engineering.
    • The languages and tools for data science are different from those of software engineering, so there needs to be some separation. But not too much, because software engineering will consume model outputs. Coordination will need to be tight.
  • Center of excellence
    • It’s common for data science teams to be at least partially centralized, with most team members reporting into a head of data science.
    • It’s important to have at least some level of centralization so data scientists can share knowledge with one another, do peer reviews, and collaborate during the creative stages of model development.
  • Embedded with product teams
    • Early on, I sat with a product team mixed with engineers, designers, and product managers. This approach failed for me because I was forced to use software engineering tools to deploy models, rather than the data science tools I was comfortable with. I decided to leave that team and join the business intelligence team to get more of the center-of-excellence benefits described above.
  • Internal modeling
    • If your data science team is building models for internal users (more of a consulting, business intelligence-type function but with machine learning), then it might not make sense for the data science team to report into engineering. But as the team scales and use cases expand into customer-facing products, engineering becomes a more natural place to live.

Team Composition

While there’s no prescription for optimum data science team composition, you should be aware of the many backgrounds and specialties within data science (and the many similar roles often confused with data science).

  • Researcher
    • If your product or solution approaches the known boundaries of machine learning, you may want to stack your team with PhD’s.
    • Examples of unsolved problems that require deep research:
  • Engineer
    • Most predictive modeling use cases do not exist on the boundaries of knowledge. They involve applying known algorithms to new contexts. Chances are, your use case falls into this category.
    • Build a team with solid foundations in statistics and programming. They will move quickly to find statistically-sound solutions using known approaches, and be capable of deploying to production.
  • Business
    • Users and product managers can’t always anticipate what is possible when machine learning is applied to your data. Have enough team members capable of working closely with the product team to assist with ideation, feasibility studies, and rapid prototypes when needed.
    • Data visualization and business communication are also crucial. Ensure you have enough team members who can tell a compelling story with data.
  • Specialties
    • Depending on your machine learning task and the type of input data you have, you may need to hire someone who specializes in one of the following.
      • Natural Language Processing (NLP) – text data
      • Computer Vision – image or video data
      • Deep Learning – massive, unstructured data sets
      • Personalization – product recommendations
      • Reinforcement Learning – robotics, navigation, finance
      • Fraud and Anomaly Detection – finance, web security, IoT
      • Time Series – finance, event data


Data science has consistently ranked as one of the best jobs in the US for many years. There’s high demand for data scientists and a stubborn shortage driven by the difficulty of acquiring all the requisite skills.

In my experience building a team from 1 to 6, you should expect the recruiting process to take anywhere from 3-6 months for each team member. The more senior the role, the longer it will take to fill.

It’s also wise to build the team incrementally vs hiring all at once. Your first hire may tell you that the data infrastructure isn’t ready yet and that you’re better off hiring more data engineers.

Whatever you do, do not hire a “Researcher” type as your first data scientist. You probably want a “Business” type data scientist who can work closely with the product team, effectively communicate challenges and progress, and evangelize data science in your org. The first 2 data scientists on our team (myself and another) both had MBAs. That business sense made all the difference in the early days.

Be Patient

Data science is inherently experimental. Even once you assemble your dream team, it may take many months (6+) before they discover and deploy a solution to your business problem.

The timeline from zero (no buy-in, no data infrastructure, no data science org structure, no data science team) to production can be extremely long. Depending on the size of your org and the maturity of its relationship with data, it could be 1.5 to 2 years.

One trick to help your team deliver value sooner, and maintain buy-in from your organization, is to ask them to knock out some quick wins. That might mean deploying a baseline model, or solving a more narrowly-scoped version of the problem.

But at the end of the day, the road to building a data science team from scratch is long, windy, and expensive. This is why AI vendors are so prolific and valuable. The build vs buy decision for AI products is a tough one, but I hope this article gives you a bit more of the information you need to make it.

Coding Data Science Python

Singleton Fails with Multiprocessing in Python

A singleton is a class designed to only permit a single instance. They have a bad reputation, but do have (limited) valid uses. Singletons present lots of headaches, and may throw errors when used with multiprocessing in Python. This article will explain why, and what you can do to work around it.

A Singleton in the Wild

Singleton usage is exceedingly rare in Python. I’ve been writing Python code for 5 years and never came across one until last week. Having never studied the singleton design pattern, I was perplexed by the convoluted logic. I was also frustrated by the errors that kept popping up when I was forced to use it.

The most frustrating aspect of using a singleton for me came when I tried to run some code in parallel with joblib. Inside the parallel processes, the singleton always acted like it hadn’t been instantiated yet. My parallel code only worked when I added another instantiation of the singleton inside the function called by the process. It took me a long time to figure out why.

Why Singletons Fail with Multiprocessing

The best explanation for why singletons throw errors with multiprocessing in Python is this answer from StackOverflow.

Each of your child processes runs its own instance of the Python interpreter, hence the singleton in one process doesn’t share its state with those in another process.

Your singleton instance won’t be shared across processes.

Working Around Singleton Errors in Multiprocessing

There are several ways to work around this problem. Let’s start with a basic singleton class and see how a simple parallel process will fail.

import time
from joblib import Parallel, delayed
class OnlyOne:
"""Singleton Class, inspired by"""
class __OnlyOne:
def __init__(self, arg):
if arg is None:
raise ValueError("Pretend empty instantiation breaks code")
self.val = arg
def __str__(self):
return repr(self) + self.val
instance = None
def __init__(self, arg=None):
if not self.instance:
self.instance = self.__OnlyOne(arg)
self.instance.val = arg
def __getattr__(self, name):
return getattr(self.instance, name)
def worker(num):
"""Single worker function to run in parallel.
Assume that this function has to do an empty
instantiation of the singleton.
one = OnlyOne()
one.val += num
return one.val
# Instantiate singleton
one = OnlyOne(0)
# Try to run in parallel
# Will hit the ValueError that raises with
# empty instantiation
res = Parallel(n_jobs=-1, verbose=10)(
delayed(worker)(i) for i in range(10)

In this example, the singleton needs to do an empty instantiation inside your worker function because we want access to some attribute stored in the singleton. We don’t know what value to instantiate it with because that’s the very thing we’re trying to access from the attribute.

Environment Variables

Here’s a simple solution I came up with that worked for me, and might for you as well. The solution here uses environment variables to store state across processes.

import time
from joblib import Parallel, delayed
import os
class OnlyOne:
"""Singleton Class, inspired by
Modified to work with parallel processes using environment
variables to store state across processes.
class __OnlyOne:
def __init__(self, arg):
if arg is None:
raise ValueError("Pretend empty instantiation breaks code")
self.val = arg
def __str__(self):
return repr(self) + self.val
instance = None
def __init__(self, arg=None):
if not self.instance:
if arg is None:
# look up val from env var
arg = os.getenv('SINGLETON_VAL')
# set env var so all workers use the same val
os.environ['SINGLETON_VAL'] = arg
self.instance = self.__OnlyOne(arg)
self.instance.val = arg
def __getattr__(self, name):
return getattr(self.instance, name)
def worker(num):
"""Single worker function to run in parallel.
Assume that this function has to do an empty
instantiation of the singleton.
one = OnlyOne()
one.val += num
return one.val
# Instantiate singleton
one = OnlyOne(0)
# Run in parallel worry-free
res = Parallel(n_jobs=-1, verbose=10)(
delayed(worker)(i) for i in range(10)

Pass Singleton as Argument

Another solution is to simply pass the instantiated singleton instance as an argument to the worker function.

import time
from joblib import Parallel, delayed
class OnlyOne:
"""Singleton Class, inspired by"""
class __OnlyOne:
def __init__(self, arg):
if arg is None:
raise ValueError("Pretend empty instantiation breaks code")
self.val = arg
def __str__(self):
return repr(self) + self.val
instance = None
def __init__(self, arg=None):
if not self.instance:
self.instance = self.__OnlyOne(arg)
self.instance.val = arg
def __getattr__(self, name):
return getattr(self.instance, name)
def worker(num, one):
"""Single worker function to run in parallel.
one.val += num
return one.val
# Instantiate singleton
one = OnlyOne(0)
# Run in parallel succeeds when one is passed
# as arg to worker
res = Parallel(n_jobs=-1, verbose=10)(
delayed(worker)(i, one) for i in range(10)
Coding Data Science Python

Popular Python Packages for Data Science

Python has a robust ecosystem of data science packages. In this article, I’ll discuss the most popular Python packages for data science, including the essentials as well as my favorite packages for visualization, natural language processing, and deep learning.

Essential Python Packages for Data Science

The vast majority of data science workflows utilize these four essential Python packages.

I recommend using Anaconda to setup your Python environment. One of it’s many benefits is that it automatically installs these 4 libraries, along with many other essential Python packages for data science.


The fundamental package for scientific computing with Python.

Numpy is foundational for data science workflows because of it’s efficient vector operations. The Numpy ndarray is a workhorse for mathematical computations in tons of useful libraries.


pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

The Pandas dataframe is the primary data object for most data science workflows. A dataframe is basically a database table, with named columns of different data types. Numpy ndarrays, in contrast, must have the same data type for each element.

Scikit Learn

Simple and efficient tools for predictive data analysis

Scikit Learn is the workhorse for machine learning pipelines. It’s built on top of Numpy and Matplotlib, and plays nice with Pandas. Scitkit Learn offers implementations of almost every popular machine learning algorithm, including logistic regression, random forest, support vector machines, k-means, and many more.


Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Matplotlib is the foundational data visualization package for Python. Pandas and Scikit Learn both have handy visualization modules that rely on Matplotlib. It’s a very flexible and intuitive plotting library. The downside is that creating complex and aesthetically pleasing plots usually requires many lines of code.

Visualization Packages

My two favorite Python visualization packages for data science are both built on top of Matplotlib.


Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Seaborn makes beautiful statistical plots with one line of code. Here’s a few of my favorite examples.

A violin plot made with the Seaborn data visualization Python package can help you visualize the distribution of a variable for various slices. The violin plot displays a box and whisker plot along with a kernel density estimate of the distribution.
A joint plot made with the Seaborn data visualization Python package can help you visualize the joint distribution of two variables. The joint plot is similar to a scatter plot, but shows the density of values instead of individual observations. It also shows the univariate distributions on each axis.

Scikit Plot

There are a number of visualizations that frequently pop up in machine learning. Scikit-plot generates quick and beautiful graphs and plots with as little boilerplate as possible.

Scikit Plot provides one-liners for many common machine learning plots, including confusion matrix heatmaps, ROC curves, precision-recall curves, lift curves, cumulative gains charts, and others. Here’s a slideshow of examples.

Natural Language Processing Packages

Natural language processing (NLP) is my specialty within data science. There’s a lot you can accomplish with Scikit Learn for NLP, which I’ll quickly mention below, but there are two additional libraries that can really help you level-up your NLP project.

Scikit Learn

CountVectorizer or TfidfVectorizer make it easy to transform a corpus of documents into a term document matrix. You can train a bag of words classification model in no time when you combine these with LogisticRegression.

Scikit Learn also provides LatentDirichletAllocation for topic modeling with LDA. I like to pair it with pyLDAvis to produce interactive topic modeling dashboards like the one below.

Screenshot of an interactive topic modeling dashboard generated by pyLDAvis.


Industrial strength natural language processing

Spacy is really powerful, and in my opinion supersedes the NLTK package that used to be the gold standard for things like part of speech tagging, dependency parsing, and named entity recognition.

Spacy does all of those for you in one line of code without any NLP knowledge. And it’s extensible to add your own entities and meta data to spans of tokens within each document.

And the displacy visualization tool is just awesome. Check out this slideshow of examples.


🤗 Transformers provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

State-of-the-art language models such as BERT and GPT-3 are trained with a neural network architecture called “transformers.” The Transformers library by Hugging Face allows you to apply these pre-trained models to your text data.

By doing so, you can generate vectors for each sentence in your corpus and use those vectors as features in your models. Or if your task is one that Transformers supports, you can just apply a complete model and be done. They currently support the following tasks.

  • Sequence classification
  • Extractive question answering
  • Language modeling
  • Named entity recognition
  • Summarization
  • Translation

Deep Learning Packages

Deep learning in Python is dominated by two packages: TensorFlow and PyTorch. There are others, but most data scientists use one of these two, and the split seems roughly equal. So if you want to train a neural network, I recommend picking TensorFlow or PyTorch.


An end-to-end open source machine learning platform

I use TensorFlow for all of my deep learning needs. The Keras high-level wrapper, which is now incorporated into TensorFlow 2.0, was what sold me on TensorFlow. It’s intuitive, reasonably flexible, and efficient.

TensorFlow is production ready with the TensorFlow Serving module. I’ve used it in combination with AWS SageMaker to deploy a neural network behind an API, but I wouldn’t describe TensorFlow Serving as particularly easy to use.


An open source machine learning framework that accelerates the path from research prototyping to production deployment

I’ve never personally used PyTorch (except as a backend for the Transformers library), so I probably won’t do it justice here. I’ve heard it described as more popular in academia. That quote above from their website suggests they are trying to change that image.

But my understanding is that PyTorch offers a bit more flexibility for designing novel neural network architectures than does TensorFlow. So if you plan to do research on new architectures, PyTorch might be right for you.

Career Data Science

What Does a Data Scientist Do?

Have you been wondering what a data scientist does? This article will clarify what a data scientist does and does not do. We’ll show why data science is such a great career, and how it compares to similar roles.

Why Data Science Is a Good Career

Data science is one of the best careers for highly technical individuals. It offers a clear path to a salary above $100,000 within 5 years. It also offers a chance to solve challenging problems, work with multi-disciplinary teams, and make a big impact.

Similar Roles That Get Confused with Data Science

The skills required of a data scientist overlap with many other roles. But there are key differences between data science and these roles in both emphasis and level of expertise required for each skill.

Here are some similar roles that often get conflated with data science.

  • Business Intelligence Analyst
    • Similarities: Both roles require analyzing data and building predictive models.
    • Differences: BI analysts are more focused on visualization and dashboards. They are also more consultative, often working with internal stakeholders to deliver insights. In contrast, data scientists are more focused on building models for production use cases (i.e. for external customers).
  • Data Engineer
    • Similarities: Both roles require building extract, transform, load (ETL) pipelines. They also both require knowledge of databases and big data.
    • Differences: Data engineers are exclusively responsible for building and maintaining data pipelines and data stores. Data scientists, in contrast, may build their own data pipelines but only as a means to an end for their model pipelines.
  • Machine Learning Engineer
    • Similarities: Both roles require knowledge of machine learning software, along with tools for deploying models to production.
    • Differences: ML engineers are exclusively responsible for deploying and maintaining production models and pipelines. Data scientists are often expected to deploy their own models as well, at least in low-volume contexts. But for larger scale products or companies, data scientists may focus on research and development while ML engineers focus on production.
  • Software Engineer
    • Similarities: Both roles require knowledge of coding best practices.
    • Differences: Software engineers are experts in software, often at a much lower or more conceptual level than data scientists. But coding best practices are critical for every data scientist.
  • Research Scientist
    • Similarities: Both roles require scientific rigor and data analysis.
    • Differences: Research scientists generally perform research that pushes boundaries. Their work typically results in either publications or application of cutting-edge research to industry problems. Data scientists are focused on solving the highest-value business problems, which may or may not require novel approaches.
  • Data Analyst / Business Analyst
    • Similarities: Both apply analytical techniques to understand trends in data.
    • Differences: Data analysts generally perform descriptive analyses that summarize historical data. Data scientists generally perform predictive analyses that forecast future outcomes or predict behavior in never-before-seen circumstances.

What Data Scientists Don’t Do

Before we dive into what data scientists do, let’s talk about what data scientists don’t do.

  • SQL all day
    • SQL is a powerful language for extracting and analyzing data stored in relational databases. Every data scientist should be proficient in SQL. But if you’re writing SQL all day, you’re probably either a Data Engineer (building data pipelines) or a Business Analyst (pulling data for descriptive analysis).
  • Excel all day
    • Excel is powerful, too, and certainly has it’s place in some data science workflows. But Excel is terrible for automation and reproducibility. It also lacks machine learning capabilities, to the best of my knowledge. If you’re serious about entering the field of data science, start learning Python or R right away.
  • Prototyping all day
    • Prototypes are invaluable for proving a concept and getting buy-in for a new feature or product. But data scientists must inevitably write production code and deploy models to production. Contrary to the beliefs of some software engineers, not all data science code is prototype-quality.
  • Academic research
    • Some data science roles have research components and lead to publications. And most data scientists must spend a non-trivial proportion of time experimenting with approaches that may ultimately lead to dead ends. But data science is not an academic exercise; at the end of the day, the goal must be to deploy models to production (and to maintain until end of life).
  • Web development
    • It’s often useful for data scientists to be familiar with web architectures and frameworks. And in the prototyping phase, it’s not unusual for a data scientist to create a simple web app. But building websites or apps is not a core responsibility of most data scientists.

What Data Scientists Do

Now that we know what a data scientist doesn’t do, let’s finally answer the titular question: what does a data scientist do? There are two famous definitions of data science that I’ll introduce first.

The Tweet

The famous data science definition by Josh Wills

The Venn Diagram

The famous data science skill set Venn diagram

The Gist

The gist of both of these definitions is this: data science is a hybrid field combining elements of statistics, software, and business. It’s inherently multi-disciplinary.

It’s worth noting something that may be obvious. It’s difficult for individuals to be proficient in all three disciplines. And it’s valuable for a company to have one person capable of doing work in all three. This is what drives low supply and high demand for data scientists in the labor market, contributing to a stubborn shortage that has persisted for years.

The Details

Now let’s drill into more details about what, specifically, data scientists do.

Data Science Foundations

If I were to teach a data science bootcamp, I would cover the following foundational topics. (Similarly, if I was a student, I would look for a program with the following topics in the curriculum.)

  • Software Engineering
    • Python
      • Basics, numpy, pandas, scikit-learn, matplotlib
      • Coding standards – PEP 8, OOP, abstraction, modularity
      • Parallelization, profiling
    • Cloud
      • AWS – S3, EC2, SageMaker, Lambda
    • Deployment
      • Flask, SageMaker
      • Python packaging, dependency management
      • Testing
    • SQL
    • Unix
      • Navigation, file manipulation, cron, screen, git
    • Git
      • Pull request workflow, code reviews
    • Development
      • Jupyter, IDEs – Atom, vscode
      • Repo templates
  • Modeling
    • Regression
      • Linear regression
    • Classification
      • Logistic regression
    • Clustering
      • K-means
    • Ensembling
      • Random forest, Gradient boosted trees
    • Other algorithms
      • Naïve Bayes
      • SVM
      • Collaborative Filtering
      • Multi-layer perceptron
    • Visualization
      • Matplotlib, seaborn
    • Big Data
      • Parallelization with joblib and multiprocessing
      • Memory limits
      • Spark
    • Model Selection
      • Pipelines
      • Hyperparameter tuning
      • Cross validation
    • Exploratory Data Analysis
    • Statistics
    • Hypothesis Testing
    • Model Evaluation
      • Metrics
      • Experimental design
    • Data quality checks
  • Business / Domain
    • Feasibility Studies
    • Choosing a target
    • Prototyping
    • Acquiring input data
    • Annotating data
    • Data Dictionaries
    • Agile
    • Assessing level of effort
    • Business communication
      • Your responsibility to make people understand
      • Executive summaries
      • Briefs
      • Setting expectations
    • Relationship with subject matter experts
    • Relationship with product managers
    • Relationship with customers / clients
    • Relationship with software engineers
    • Balancing short-term business value and research
      • Breakthroughs vs incremental progress
    • Helping business team understand uncertainty and non-linearity
      • Success not guaranteed, may be dependent on breakthroughs
    • Balancing interpretability and accuracy
    • Defining business metrics
    • Levels of maturity
      • Modeling efforts progress through different levels of maturity, each needs to be managed accordingly

Advanced Topics

Finally, let’s just mention some of the advanced topics in the field of data science that you may have heard as buzz words. Each of these topics could be considered a specialization within data science, to be mastered once you’re proficient in all of the foundations. I’ll leave descriptions of each for future posts.

  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • ML Engineering
  • Big Data
  • Time Series
  • Fraud and Anomaly Detection
  • Personalization
  • Reinforcement Learning
  • Data Science Leadership

Become a Data Scientist

So those are the things a data scientist does every day. No one said it was easy. But I will say that it’s a ton of fun, and an extremely rewarding career.

There’s always more to learn, and that’s why Skillenai was born – to help every data scientist at every level continue growing their career.

Stay tuned as we add tutorials for all of the topics mentioned here and beyond.