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.