Senior Data Scientist
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Job Description
Senior Data Scientist responsible for designing and implementing machine learning models to drive business insights. Collaborates with cross-functional teams to leverage data for decision-making and analysis.
Responsibilities:
- Identifies business trends and problems through complex data analysis
- Interprets results from multiple sources using a variety of techniques
- Designs, develops, and implements high-impact, scalable business solutions
- Partners cross-functionally to translate business needs into analytical frameworks and actionable insights
- Leverage expertise in handling large, complex datasets to perform exploratory data analysis, feature engineering, and statistically sound sample design
- Build, validate, deploy, and enhance complex predictive and machine learning models
- Design, build, deploy, and monitor machine learning models in production environments
- Implement model lifecycle management best practices
- Automate feedback loops for algorithms and models in production
- Design and execute experiments to evaluate model performance and business impact
- Act as a technical subject matter expert and mentor junior data scientists
Requirements:
- Bachelor’s degree or equivalent experience in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Economics, or related discipline
- Master’s or PhD preferred
- 5–7+ years of professional experience building, validating, deploying, and monitoring predictive and machine learning models in production environments
- Strong programming expertise in Python with hands-on experience building end-to-end ML solutions
- Experience with MLflow (or similar model lifecycle tools) for experiment tracking, model versioning, and deployment management
- Experience working with SQL and large-scale data processing frameworks such as Apache Spark
- Experience designing scalable data solutions in modern data platforms; experience with Microsoft Fabric is a plus
- Proven experience applying machine learning techniques (e.g., regression, classification, clustering, ensemble methods, NLP, etc.)
- Strong communication skills with the ability to explain complex analytical concepts to non-technical stakeholders.
Benefits:
- Health insurance
- Dental insurance
- Vision insurance
- 401(k) matching
- Paid time off
- Paid sick leave
- Employee stock purchase plan
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