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Senior Data Scientist
Haystack News. Build statistical and machine learning models to improve content discovery and user engagement .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in building statistical and machine learning models to enhance content discovery and user engagement, with a strong focus on causal inference methods and experimental design to drive product improvements. Proficient in translating analytical insights into actionable product decisions using advanced tools and technologies.
Highest-signal resume keywords
PhD Or M.S. In Computer ScienceMachine Learning Model DevelopmentCausal Inference MethodsPython Analytics StackSQL Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical InferenceExperimental DesignHypothesis TestingPower/Sample Size CalculationsVariance ReductionLarge-Scale Online Ranking SystemsDeep LearningGenAI DevelopmentA/B TestingData Analysis
Tools & Technologies
PandasNumPyStatsmodelsScikit-LearnLightGBMXGBoostPostgresSnowflakeDbtAirflow
Industry Keywords
Data ScienceMachine LearningConsumer-Facing ProductsUser EngagementProduct Change Attribution
Tech Stack
Tools & technologiesAirflowNumpyPandasPostgresPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Build statistical and machine learning models to improve content discovery and user engagement
- Work closely with ML engineers to translate models and insights into production systems
- Apply analytical skills to dive deep into data and find key business insights
- Apply causal inference methods to understand the impact of potential product changes
- Define and build new ML features using text and multimodal embeddings and GenAI
- Validate offline learnings with online outcomes through A/B testing; design, execute, and analyze experiments to prove product change attribution
Requirements
What you’ll need- PhD or M.S. in Computer Science, Mathematics, Electrical Engineering, Statistics, Economics or Operations Research with 5+ years of professional experience in data science, machine learning or related quantitative field
- 3+ years of professional experience with large-scale online ranking/recommender systems
- Deep expertise in statistical inference and experimental design: hypothesis testing, power/sample size calculations, variance reduction, etc.
- Proficiency in causal inference methods to measure product impact
- Proven ability to translate offline analysis into product decisions and measurable improvements in online metrics
- Fluency in the Python analytics stack (pandas, NumPy), statistical modeling (statsmodels or scikit-learn), and machine learning packages such as LightGBM and XGBoost
- Strong experience with SQL (e.g. postgres, snowflake, etc.)
- Experience working on consumer-facing products with millions of users
- Hands-on experience with orchestration/transformation tools (e.g. dbt and Airflow)
- Experience with deep learning and familiarity with PyTorch or TensorFlow
- Hands-on development of products/tools incorporating GenAI, LLMs, RAG, and/or Agents