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.