Data Scientist

Data Scientist Essential : . B.E. / B.Tech/ M. Tech./M.Sc. in any stream Experience - 4-7 Years Responsibilities: . Data Solutions Architecture: Develop innovative data-driven solutions for business challenges using Telematics/Time series data. Collaborate with domain experts to gain automotive insights. . IoT Device Mastery: Understand the Telematics Control Unit (TCU) and the time-series data it generates. . Data Landscape Analysis: Evaluate data adequacy and establish a comprehensive understanding of the data landscape. . Data Preparation: Clean and prepare datasets for modeling. Engage in ETL processes and apply data transformation techniques such as resampling, filtering, and encoding. . Exploratory Data Analysis: Conduct exploratory data analysis to derive insights. Present descriptive statistics and insights to domain experts. Identify meaningful patterns, detect seasonality and trends, and establish cause-and-effect relationships. . Feature Engineering: Design and select features, study feature importance, and decide on the machine learning strategy. . Model Development: Select appropriate machine learning/deep learning models, set up data pipelines for model training, perform hyper-parameter tuning, validation, and testing. Apply ensemble modeling techniques if required. . Reporting & Visualization: Create comprehensive reports and visualize data using plots and heat maps. Essential: . Machine Learning Expertise: Experience with machine learning algorithms (e.g., Generalized Linear Models, Boosting, Decision Trees, Neural Networks, SVM, Bayesian Methods, time series models). . Hands-on Experience: Proficiency in using machine learning models for regression, classification, and unsupervised learning algorithms. . Cloud Computing: Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. . Programming Skills: Strong programming skills in Python, with experience using libraries like pandas, numpy, matplotlib, and sklearn. . Data Visualization: Proficiency in data visualization techniques and tools. . MLOps: Exposure to MLOps and model deployment in production environments. . SQL skills: Experience working with relational and non-relational databases. Desirable: . Databricks Platform: Experience with Databricks for big data processing and machine learning. . Distributed Computing: Experience with Spark or other distributed computing frameworks. . AutoML Tools: Understanding of tools like AWS Sagemaker, Databricks AutoML and IBM AutoAI. . Telematics Data Analytics: Experience in time-series/IoT data analytics, including data streaming from vehicle on-board IoT devices. . Automotive Systems Knowledge: Exposure to automotive systems, basics of automobiles, and Controller Area Network (CAN) protocol. . Remote Collaboration: Experience working with remote team members. . Advanced Visualization Tools: Experience with data visualization tools such as Tableau and PowerBI.