Data Scientist_ML
New
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningPandasTensorflowDatabricksMLOpsPySpark
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- 5+ years of experience in machine learning and data science roles.
- Strong foundation in probability, statistics, regression analysis, and machine learning algorithms.
- Advanced programming skills in Python, including libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow or similar frameworks.
- Strong experience with SQL and working with large-scale datasets and data warehouses.
- Hands-on experience with PySpark and distributed data processing systems.
- Experience building and deploying ML models using APIs and frameworks such as Flask or similar tools.
- Domain experience in retail, CPG, or e-commerce with expertise in demand forecasting, pricing, and promotion analytics.
- Experience with MLOps tools and platforms such as Databricks, AWS SageMaker, Azure ML, or Google Vertex AI.
- Strong communication skills with the ability to translate complex models into business insights.
Responsibilities
- Design, develop, and deploy machine learning models focused on pricing optimization, demand forecasting, promotion effectiveness, and revenue forecasting.
- Build and enhance statistical and ML models including demand forecasting, price elasticity, and inventory optimization solutions.
- Develop scalable ML pipelines and production-ready systems using frameworks such as Databricks and cloud-based MLOps platforms.
- Design, evaluate, and improve time series forecasting models, including advanced approaches such as transformer-based architectures.
- Analyze large-scale structured and unstructured retail datasets to generate actionable business insights and pricing strategies.
- Implement model monitoring, experimentation frameworks, and continuous improvement processes for ML systems.
- Collaborate with cross-functional teams including engineering, product, and business stakeholders to deliver scalable AI solutions.
- Mentor junior data scientists and contribute to best practices across data science and ML engineering teams.
- Communicate findings, insights, and recommendations clearly to both technical and non-technical stakeholders.
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