Experience Required : 5+ Years in Data Science & Machine Learning (Minimum 4+ Years dedicated to Statistical Modeling, Predictive Analytics & Enterprise Data Warehousing)

Location : Chennai - Hybrid

Notice Period : Immediate to 15-30 Days Max

Education Required : Bachelor's or Master's Degree in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or related quantitative engineering discipline

Job Summary :

We are seeking an execution-focused Senior Data Scientist & Predictive Analytics Specialist to take ownership of extracting actionable intelligence, forecasting critical business metrics, and uncovering complex behavioral patterns from massive enterprise datasets. In this role, you will bridge statistical theory with applied algorithmic modeling-designing, benchmarking, and operationalizing machine learning algorithms that directly inform strategic business decisions and optimize revenue outcomes.

The ideal candidate brings 5+ years of practical experience formulating quantitative hypotheses, engineering robust predictive and prescriptive models, and manipulating large-scale multi-terabyte data stores using Google BigQuery, advanced SQL, and Python. You will oversee the full lifecycle of data experimentation-from exploratory data analysis and automated data augmentation to model validation, metric tracking, and deployment automation.

Key Responsibilities :

- Predictive & Prescriptive Modeling : Design, train, validate, and fine-tune supervised, unsupervised, and ensemble machine learning models (e.g., gradient boosting, regression, classification, clustering, time-series forecasting) to address core business use cases.

- Large-Scale Data Ingestion & Transformation : Extract, cleanse, aggregate, and augment multi-source tabular and event-driven data; write and optimize analytical queries directly in Google BigQuery and modern relational databases.

- Hypothesis Testing & Statistical Analysis : Apply rigorous mathematical and statistical methodologies (A/B testing, causal inference, multivariate regression, variance analysis) to validate experiments and uncover actionable patterns.

- Feature Engineering & Scalable Data Pipelines : Engineer automated data loading, feature transformation, and data augmentation workflows designed to maximize algorithmic lift and ensure model robustness in automated production settings.

- Continuous Research & Algorithmic Optimization : Benchmark emerging machine learning libraries, analytics tools, and algorithmic frameworks to improve model accuracy, inference speed, and computational efficiency.

- Stakeholder Storytelling & Decision Enablement : Translate unstructured business problems into concrete analytical formulations; present key findings, model limitations, and prescriptive recommendations to both technical pods and executive stakeholders.

Skills & Qualifications :

Required Technical DNA :

- Total Experience : 5+ years of professional hands-on experience in Data Science, Machine Learning, and Advanced Analytics.

- Analytics & Modeling Core : Deep theoretical and practical knowledge of Machine Learning algorithms, predictive modeling, statistical inference, and experimental design.

- Large-Scale Querying : Proven hands-on expertise with Google BigQuery (query optimization, analytical window functions, data transformation at scale).

- Programming & Tooling : Strong proficiency in Python and its scientific/ML stack (Scikit-learn, Pandas, NumPy, SciPy, XGBoost/LightGBM) along with advanced SQL.

- Problem Formulation : Proven ability to engage with non-technical business partners, extract analytical requirements, and define quantitative KPIs.

- Working exposure to BigQuery ML (BQML) for training and scoring models directly inside data warehouses.

- Experience with distributed computing frameworks (PySpark, Dataproc) or pipeline orchestration tools (Apache Airflow).

- Familiarity with MLOps practices, model registry management (e.g., Vertex AI, MLflow), and containerized inference endpoints.