Job Description
About The Role
The role owns the end-to-end data science lifecycle, turning complex data sets into predictive models and actionable business insights at scale.
The team works closely with product and engineering stakeholders to solve high-impact problems using advanced statistical methods and machine learning.
Key Responsibilities
- Develop and deploy machine learning models and predictive algorithms using Python and SQL for core business applications
- Design and execute rigorous A/B tests to measure the impact of product features and algorithmic updates
- Clean, transform, and analyze large-scale data sets using PySpark, Pandas, and cloud data warehouses like Snowflake or BigQuery
- Collaborate with data engineers to build robust feature pipelines and ensure data quality across training and inference systems
- Communicate complex technical findings and model performance metrics clearly to cross-functional leadership and stakeholders
What We Are Looking For
- 3-6 years of experience in data science, quantitative analysis, or machine learning, with a strong track record of deploying models to production
- Advanced proficiency in Python, SQL, and core data science libraries such as scikit-learn, Pandas, NumPy, and Statsmodels
- Demonstrated experience with statistical testing, experimental design, and hypothesis testing in production environments
- Solid understanding of relational databases, distributed computing, and data warehouse architecture
- BS or MS in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field
- Bonus: Experience with time-series forecasting, causal inference methods, or foundational LLM applications
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