AI Data Scientist Sr
About The Position
As a Data Scientist, you will be a key player in the entire model lifecycle, from exploring raw business data to deploying production-ready AI agents. We will count on you to be passionate about leveraging machine learning to solve real-world business problems and thrive in an agile, fast-paced environment. Along the way, you will get to: Core Modeling & Analysis: Develop and tune a range of machine learning models, including regression, classification, and time-series forecasting. Apply your expertise to solve critical business problems, such as predicting inventory needs and scoring the likelihood of an opportunity closing. Perform in-depth exploratory data analysis using PySpark and SQL to uncover key insights and understand complex business data. Feature Engineering: Create powerful, predictive features from diverse raw data sources (e.g., sales history, client activity) to enhance model accuracy and performance. Utilize the Databricks Feature Store to manage and share model features, ensuring consistency and reusability across projects. Model Validation & Interpretation: Validate model performance using robust statistical methods, ensuring accuracy and reliability. Articulate the drivers behind your models' predictions and communicate complex findings clearly to both technical and non-technical stakeholders. MLOps & Production Modeling: Champion best practices in MLOps, with hands-on experience using MLflow for the end-to-end model lifecycle, including experiment tracking, versioning, and registry management. Design and build models for automated retraining and deployment using Databricks Jobs & Workflows. Collaborate with engineers and stakeholders to productionize models, ensuring seamless integration and scalability. Be AmbITious: This opportunity is not just about what you do today but also about where you can go tomorrow. When you bring your hunger, heart, and harmony to Insight, your potential will be met with continuous opportunities to upskill, earn promotions, and elevate your career.
Requirements
- Proven experience as a Data Scientist with a portfolio of completed projects.
- Python: Expert proficiency with core data science libraries such as Pandas for data manipulation and scikit-learn for machine learning models.
- Data & Analytics: Strong skills in SQL and PySpark for data exploration and feature engineering.
- MLOps: Direct, hands-on experience with the Databricks platform, specifically Databricks Feature Store, Databricks Jobs & Workflows, and MLflow.
- Problem-Solving: Strong analytical and problem-solving skills with the ability to translate ambiguous business problems into well-defined data science solutions.
- Communication: Excellent verbal and written communication skills with the ability to present complex technical findings to diverse audiences.
- Education: Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
Responsibilities
- Develop and tune a range of machine learning models, including regression, classification, and time-series forecasting.
- Apply your expertise to solve critical business problems, such as predicting inventory needs and scoring the likelihood of an opportunity closing.
- Perform in-depth exploratory data analysis using PySpark and SQL to uncover key insights and understand complex business data.
- Create powerful, predictive features from diverse raw data sources (e.g., sales history, client activity) to enhance model accuracy and performance.
- Utilize the Databricks Feature Store to manage and share model features, ensuring consistency and reusability across projects.
- Validate model performance using robust statistical methods, ensuring accuracy and reliability.
- Articulate the drivers behind your models' predictions and communicate complex findings clearly to both technical and non-technical stakeholders.
- Champion best practices in MLOps, with hands-on experience using MLflow for the end-to-end model lifecycle, including experiment tracking, versioning, and registry management.
- Design and build models for automated retraining and deployment using Databricks Jobs & Workflows.
- Collaborate with engineers and stakeholders to productionize models, ensuring seamless integration and scalability.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
