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.