Padmi

Sr Data Scientist Mumbai (India)

MumbaiPosted 1 month ago
Data Science And StatisticsSeniorFull Time; Regular
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Role Overview We are looking for a Senior Data Scientist to design, build, and deploy advanced machine learning models for industrial applications. The role will focus on transforming complex operational, sensor, production, and process data into reliable predictive and prescriptive solutions that improve efficiency, quality, safety, and decision-making. The ideal candidate has robust experience in applied machine learning, statistical modeling, industrial data, and production-grade model development. Key Responsibilities Develop, validate, and deploy machine learning models for industrial use cases such as predictive maintenance, process optimization, anomaly detection, quality prediction, demand forecasting, and asset performance monitoring. Work with large-scale structured and time-series data from industrial systems, sensors, machines, production lines, and operational platforms. Collaborate with engineers, domain experts, product teams, and business stakeholders to identify high-value machine learning opportunities. Translate industrial challenges into data science problems and deliver practical, scalable solutions. Build robust data pipelines, feature engineering workflows, and model evaluation frameworks. Monitor model performance in production and support continuous improvement of deployed models. Communicate model insights, assumptions, limitations, and business impact clearly to technical and non-technical stakeholders. Required Qualifications Masters or PhD in Data Science, Computer Science, Statistics, Mathematics or a related field. Strong qualified experience as a Data Scientist, Machine Learning Engineer, or similar role. Proven experience building machine learning models for real-world, production-oriented use cases. Solid knowledge of supervised and unsupervised learning, time-series modeling, anomaly detection, optimization, and statistical analysis. Experience working with industrial, manufacturing, energy, logistics, process, .

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