Senior Data Scientist

Job description

**Job Role & 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. **Technical Skills / Experience 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. **Behavioral:** - - Analytical Thinking: Strong analytical skills to interpret complex data and derive actionable insights. - Problem-Solving: Ability to approach problems creatively and develop innovative solutions. - Attention to Detail: Meticulous attention to detail to ensure data accuracy and model reliability. - Communication: Excellent communication skills to convey technical information to non-technical stakeholders. - Collaboration: Strong team player with the ability to work collaboratively in a cross-functional environment. - Adaptability: Flexibility to adapt to changing business needs and evolving technologies. - Curiosity: A proactive learner with a curiosity to explore new technologies and methodologies. - Time Management: Effective time management skills to handle multiple projects and meet deadlines **Essential :** B.E. / B.Tech/ M. Tech./M.Sc. in any stream.