Data Science and Machine Learning Senior Associate

Position Title: Data Science and Machine Learning Senior Associate

Experience Required:
Senior Associate Exp: 3 to 5 years experience in relevant field

Location - Chennai

Education Required:
Bachelor's Degree, Bachelor's Degree, Bachelor's Degree
Education Preferred:
Master's Degree

Position Description:
What you’ll be able to do: As a Data Scientist, you will design and deliver production-grade solutions that power our global supply-demand matching systems. This is a cross-disciplinary role where you will sit at the intersection of Data Science and Software Engineering. You will go beyond theoretical modeling to build scalable, end-to-end data products that directly influence Ford’s strategic direction. The primary focus of this role is high-accuracy demand forecasting and predictive modeling. You will be expected to navigate ambiguity, translate business problems into analytical formulations, and contribute to a culture of technical rigor. A key part of your success will be your ability to collaborate closely with software engineers and product owners, translating complex ML concepts into actionable technical solutions. • Demand Forecasting & Predictive Modeling: Develop, implement, and validate algorithms (e.g., Time-Series, Causal Inference, Optimization) specifically tailored to demand forecasting and supply-chain efficiency. • Cross-Functional Collaboration: Partner actively within a cross-disciplinary team to deliver analytics software. Contribute to technical specifications, participate in code reviews, and socialize results with non-technical business partners to foster data-driven decision-making. • MLOps & Reliability: Build and maintain data-science pipelines for scalable deployment and retraining. Support the health of your models in production, including setting up proactive alerting and monitoring for data drift or bias. • Visualization: Create visualizations to connect disparate data, find patterns, and tell engaging stories regarding market trends and supply constraints using applications such as QlikSense. • Full-Lifecycle Ownership: Own the development process from data sourcing (ETL) and exploration to deployment, monitoring, and iterative improvement. Key Responsibilities: 1) Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making 2) Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making 3) Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends 4) Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation 5) Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
Skills Required:
Big Query,, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API, Data Science, Machine Learning, Data/Analytics
Skills Preferred:
Java

Experience Preferred:
• Technical Experience: 3+ years of hands-on experience with Python, SQL, and Machine Learning/Optimization techniques. • Technical Domain Expertise: Expertise in Predictive Analytical Methods (e.g., Neural Networks, Ensemble Methods, Support Vector Machines) with experience in demand forecasting or market trend analysis. • Production Experience: 2+ years of experience applying advanced statistical methods (e.g., Multivariate Analysis, Regressions, Cluster Analysis) within production-grade software environments. • Quality Focus: Experience with testing strategies for ML, including bias detection and robustness checks. • Software Engineering: Experience deploying and maintaining production software.