Data Scientist

We are seeking a skilled Data Scientist with 2 to 5 years of experience, specializing in Machine Learning, PySpark, and Data bricks, with a proven track record in long-range demand and sales forecasting. This role is crucial for the development and implementation of an automotive OEM's next-generation Intelligent Forecast Application. The position will involve building, optimizing, and deploying large-scale machine learning models for complex, long-term forecasting challenges using distributed computing frameworks, specifically PySpark on the Data bricks platform. The work will directly support strategic decision-making across the automotive value chain, including areas like long-term demand planning, production scheduling, and inventory optimization. The ideal candidate will have hands-on experience developing and deploying ML models for forecasting, particularly long-range predictions, in a production environment using PySpark and Data bricks. This role requires strong technical skills in machine learning, big data processing, and time series forecasting, combined with the ability to work effectively within a technical team to deliver robust and scalable long-range forecasting solutions. Role & Responsibilities: Machine Learning Model Development & Implementation for Long-Range Forecasting: Design, develop, and implement scalable and accurate machine learning models specifically for long-range demand and sales forecasting challenges. Data Processing and Feature Engineering with PySpark: Build and optimize large-scale data pipelines for ingesting, cleaning, transforming, and engineering features relevant to long-range forecasting from diverse, complex automotive datasets using PySpark on Data bricks. Deployment and MLOps on Data bricks: Develop and implement robust code for model training, inference, and deployment of long-range forecasting models directly within the Data bricks platform. Performance Evaluation & Optimization: Evaluate long-range forecasting model performance using relevant metrics (e.g., MAE, RMSE, MAPE, considering metrics suitable for longer horizons) and optimize models and data processing pipelines for improved accuracy and efficiency within the PySpark/Data bricks ecosystem. Work effectively as part of a technical team, collaborating with other data scientists, data engineers, and software developers to integrate ML long-range forecasting solutions into the broader forecasting application built on Data bricks. Communicate technical details and forecasting results effectively within the technical team. Requirements Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a closely related quantitative field. 2 to 5 years of hands-on experience in a Data Scientist or Machine Learning Engineer role. Proven experience developing and deploying machine learning models in a production environment. Demonstrated experience in long-range demand and sales forecasting. Significant hands-on experience with PySpark for large-scale data processing and machine learning. Extensive practical experience working with the Data bricks platform, including notebooks, jobs, and ML capabilities Expert proficiency in PySpark. Expert proficiency in the Data bricks platform. Strong proficiency in Python and SQL. Experience with machine learning libraries compatible with PySpark (e.g., MLlib, or integrating other libraries). Experience with advanced time series forecasting techniques and their implementation. Experience with distributed computing concepts and optimization techniques relevant to PySpark. Hands-on experience with a major cloud provider (Azure, AWS, or GCP) in the context of using Data bricks. Familiarity with MLOps concepts and tools used in a Databricks environment. Experience with data visualization tools. Analytical skills with a deep understanding of machine learning algorithms and their application to forecasting. Ability to troubleshoot and solve complex technical problems related to big data and machine learning workflows.

Bot Consulting Private Limited