Job Description

Role: Sr Data Scientist - Digital & Analytics Experience: 7+ Years | Industry: Exposure to manufacturing, energy, supply chain or similar Location: On-Site @ Bharuch, Gujarat (6 days/week, Mon-Sat working) Perks: Work with Client Directly & Monthly renumeration for lodging Mandatory Skills: Exp. In full scale implementation from requirement gathering till project delivery (end to end). EDA, ML Techniques (supervised and unsupervised), Python (Pandas, Scikit-learn, Pyomo, XGBoost, etc.), cloud ML tooling (Azure ML, AWS Sage maker, etc.), plant control systems (DCS, SCADA, OPC UA), historian databases (PI, Aspen IP.21), and time-series data, optimization models (LP, MILP, MINLP). We are seeking a highly capable and hands-on Sr Data Scientist to drive data science solution development for chemicals manufacturing environment. This role is ideal for someone with a strong product mindset and a proven ability to work independently, while mentoring a small team. You will play a pivotal role in developing advanced analytics and AI/ML solutions for operations, production, quality, energy optimization, and asset performance, delivering tangible business impact. Responsibilities: 1. Data Science Solution Development o Design and develop predictive and prescriptive models for manufacturing challenges such as process optimization, yield prediction, quality forecasting, downtime prevention, and energy usage minimization. o Perform robust exploratory data analysis (EDA) and apply advanced statistical and machine learning techniques (supervised and unsupervised). o Translate physical and chemical process knowledge into mathematical features or constraints in models. o Deploy models into production environments (on-prem or cloud) with high robustness and monitoring. 2. Team Leadership & Management o Lead a compact data science pod (2-3 members), assigning responsibilities, reviewing work, and mentoring junior data scientists or interns. o Own the entire data science lifecycle: problem framing, model development, and validation, deployment, monitoring, and retraining protocols. 3. Stakeholder Engagement & Collaboration o Work directly with Process Engineers, Plant Operators, DCS system owners, and Business Heads to identify pain points and convert them into use-cases. o Collaborate with Data Engineers and IT to ensure data pipelines and model interfaces are robust, secure, and scalable. o Act as a translator between manufacturing business units and technical teams to ensure alignment and impact. 4. Solution Ownership & Documentation o Independently manage and maintain use-cases through versioned model management, robust documentation, and logging. o Define and monitor model KPIs (e.g., drift, accuracy, business impact) post-deployment and lead remediation efforts. Required Skills: 1. 7+ years of experience in Data Science roles, with a strong portfolio of deployed use-cases in manufacturing, energy, or process industries. 2. Proven track record of end-to-end model delivery (from data prep to business value realization). 3. Master's or PhD in Data Science, Computer Science Engineering, Applied Mathematics, Chemical Engineering, Mechanical Engineering, or a related quantitative discipline. 4. Expertise in Python (Pandas, Scikit-learn, Pyomo, XGBoost, etc.), and experience with cloud ML tooling (Azure ML, AWS Sagemaker, etc.). 5. Familiarity with plant control systems (DCS, SCADA, OPC UA), historian databases (PI, Aspen IP.21), and time-series data. 6. Experience in developing optimization models (LP, MILP, MINLP) for process or resource allocation problems is a strong plus.

Job Types: Full-time, Contractual / Temporary
Contract length: 6-12 months

Pay: Up to ?200,000.00 per month

Work Location: In person