Salary
💰 $138,900 - $186,200 per year
Tech Stack
AirflowPythonScikit-LearnSparkSQLTensorflow
About the role
- Improve title-level content engagement forecasting models and create models for content targeting
- Apply state of the art machine learning and statistical analysis to develop models related to opportunity sizing, targeting, and optimization
- Analyze user behavioral data to identify patterns, uncover opportunities, and create common understanding of how people are interacting with the service and content
- Develop prototype solutions, mathematical models, algorithms, and robust analytics leading to impactful insights communicated clearly and visually
- Partner closely with business stakeholders to identify and unlock opportunities, and with other data teams to improve platform capabilities around data modeling, data visualization, experimentation and data architecture
Requirements
- Bachelor’s degree in advanced Mathematics, Statistics, Data Science or comparable field of study
- 5+ years related experience with developing machine learning models and conducting statistical analysis
- Deep understanding of machine learning concepts and statistical methods
- Ability to draw insights and conclusions from data to inform model development and business decisions
- Desire to collaborate with other data scientists, data analysts, and key business partners
- Proficient in analyzing data and developing ML models using Python (with ML frameworks like tensorflow, scikit-learn, etc.)
- Capable of reading and writing Object-Oriented code to review and implement modeling pipelines (Airflow, Luigi, etc.)
- Understanding of SQL and distributed data technologies (Hive, Spark)
- NICE TO HAVES: MS or PhD in Computer Science, Engineering, Statistics, Economics, Physics or related quantitative field (e.g. Econometrics, Mathematics, Operations Research)
- NICE TO HAVES: Working knowledge of Bayesian modeling and familiarity with probabilistic programming packages/language such as Stan, PyMC
- NICE TO HAVES: Experience fully integrating data science solutions into the business and operationalizing ML models