For fitness app, train machine learning models for our workout users and for internal

business forecasting; design and analyze A/B tests; and extract causal insights from observational

fitness data. Work with the Product team to help them ideate, design, and prioritize product

improvements. Work with the Product team to help them understand user behavior. Work with

the Client and Server teams to launch experiments and ship product improvements. Work with

the Data Engineering team to help them organize, document, and clean our data. Work with the

Marketing team to help them understand the results of their campaigns. Work with the Finance

team to help them forecast financial indicators. Work with the Customer Success team to help

them make sense of product issues and customer complaints. Ideate, propose, design, monitor,

and interpret the results of A/B tests. Train machine learning models that improve our workout

generator, business workflows, and business decision-making. Leverage our data to ideate

improvements to our workout generator. Leverage exercise science to ideate improvements to

our workout generator. Build dashboards that help other teams leverage our data for decision-

making. Set up automated alerts to monitor critical business metrics.

Master’s degree in Statistics, Data Science, or a related field.

1) Background in using Python and statistical Python packages including pandas,

NumPy, and scikit-learn for exploratory data analysis, data cleaning and preprocessing.

2) Background in using supervised and unsupervised machine learning models including linear

regression, logistic regression, random forests, and k-means clustering, for predictive analytics and

pattern recognition in large datasets.

3) Background in SQL for relational databases, including

complex SQL queries for data extraction, transformation, and loading (ETL), and developing and

optimizing queries to efficiently manage and manipulate large volumes of data.

4) Background in using data visualization tools, including Python visualization libraries Matplotlib, Seaborn, and

Plotly, to create interactive and static visualizations to present complex data in a clear and

insightful manner.

5) Background in experimentation and A/B testing, both of which must include

econometrics, conducting controlled experiments, using hypothesis testing to determine

statistical significance, evaluating experiment outcomes, and making statistical conclusions.

6) Knowledge of distributed computing principles and frameworks, including Apache Hadoop or

Spark, proficiency in handling large datasets and big data ecosystems.

7) Knowledge of linear

regression and time series modeling using techniques ARIMA and SARIMA and exponential

smoothing. Use of these models to forecast trends, identify seasonal patterns, and make informed

predictions based on historical data.

The person who fills this role may work remotely from anywhere in the U.S. (HQ: San

Francisco, CA)