End-to-End Model Development: Independently lead data science projects through the entire lifecycle: from problem definition and data acquisition to feature engineering, model selection, and deployment.
Predictive Modeling & Machine Learning: Design, build, and validate robust machine learning models (e.g., regression, classification, clustering, time-series forecasting) to solve key business challenges in R&D, product innovation, and quality.
Advanced Analytics & Insights: Apply statistical inference, data mining, and advanced analytical techniques to extract actionable insights from complex structured and unstructured data (e.g., text, sensor data).
Machie Learning Ops & Scalability: Build and maintain data and modeling pipelines. Deploy models into production environments and monitor their performance, ensuring scalability and reliability. Working experience on Computer-vision/Image analysis projects.
Technical Innovation: Stay current with advances in data science and machine learning. Evaluate and implement new tools, frameworks (e.g., PyTorch, TensorFlow, Keras, scikit-learn), and methodologies to enhance the team's capabilities.
Business Partnership (15%)
Work effectively with clients to identify client needs and success criteria, and translate into clear project objectives, timelines, and plans.
Be responsive and timely in sharing project updates, responding to client queries, and delivering on project commitments.
Clearly communicate analysis, conclusions, insights, and conclusions to clients using written reports and real-time meetings.
Innovation & Continuous Improvement (10%)
Improve processes and methodologies
Develop new analytical capabilities
Continuously upskill in ML and AI best practices
Administration (5%)
Complete required trainings and organizational responsibilities
MINIMUM QUALIFICATIONS
Education: Master’s or Ph.D. in Data Science, Computer Science, Statistics, or related quantitative field
Experience: 5+ years building and deploying ML models and working experience on Agentic AI
Technical Skills: Strong ML expertise (feature engineering, validation, ensemble models, neural networks)
Proficiency in Python/R (pandas, NumPy, scikit-learn, etc.)
Experience with PyTorch, TensorFlow, Keras, or similar
Hands-on MLOps, Git, and cloud platforms (AWS/GCP/Azure)
Experience deploying production ML systems
Strong data storytelling and visualization skills (Shiny, Dash, Tableau)
Ability to manage multiple projects independently
PREFERRED QUALIFICATIONS
Certifications in R, Python, or SQL
Applicants must meet minimum age qualifications in the country in which the job is located.
