Senior Data Scientist

CTC-

Industry preferred

Expertise in Python (Pandas, Scikit-learn, Pyomo, XGBoost, etc.), and experience with cloud ML tooling (Azure ML).

Responsibilities :

Data Science Solution Development :

• Design and develop predictive and prescriptive models for manufacturing challenges such as process optimization, yield prediction, quality forecasting, downtime prevention, and energy usage minimization.
• Perform robust exploratory data analysis (EDA) and apply advanced statistical and machine learning techniques (supervised and unsupervised).
• Translate physical and chemical process knowledge into mathematical features or constraints in models.
• Deploy models into production environments (on-prem or cloud) with high robustness and monitoring

Team Leadership & Management :

• Lead a compact data science pod (2-3 members), assigning responsibilities, reviewing work, and mentoring junior data scientists or interns.
• Own the entire data science lifecycle: problem framing, model development, validation, deployment, monitoring, and retraining protocols.

Stakeholder Engagement & Collaboration :

• Work directly with Process Engineers, Plant Operators, DCS system owners, and Business Heads to identify pain points and convert them into use-cases.
• Collaborate with Data Engineers and IT to ensure data pipelines and model interfaces are robust, secure, and scalable.
• Act as a translator between manufacturing business units and technical teams to ensure alignment and impact.

Solution Ownership & Documentation :

• Independently manage and maintain use-cases through versioned model management, robust documentation, and logging.
• Define and monitor model KPIs (e.g., drift, accuracy, business impact) post-deployment and lead remediation efforts.

Desired profile:

• 8+ years of experience in Data Science roles, with a strong portfolio of deployed use-cases in manufacturing, energy, or process industries.

• Proven track record of end-to-end model delivery (from data prep to business value realization).
• Masters or PhD in Data Science, Computer Science Engineering, Applied Mathematics, Chemical Engineering, Mechanical Engineering, or a related quantitative discipline.
• Expertise in Python (Pandas, Scikit-learn, Pyomo, XGBoost, etc.), and experience with cloud ML tooling (Azure ML, AWS Sagemaker, etc.).
• Familiarity with plant control systems (DCS, SCADA, OPC UA), historian databases (PI, Aspen IP.21), and time-series data.
• D perience in developing optimization models (LP, MILP, MINLP) for process or resource allocation problems is a strong plus. D