Data Science Intern (Risk Analytics, Data Ops & Automation)

Data Science Intern (Risk Analytics, Data Ops & Automation)

Location:

About

IoT, telematics, advanced analytics, machine learning, GenAI, and workflow automation

Role Overview

Data Science Intern

Key Responsibilities

1. Risk Analytics

• Assist in statistical analysis and exploratory data analysis (EDA) across large telematics datasets.
• Conduct pattern recognition and anomaly detection for fuel, GPS, OBD, and video analytics data.

2. Data Annotation &

• Maintain labelling quality benchmarks and annotation consistency at scale.
• Work with automated labelling tools and create/maintain labelling SOPs.

3. Data Pipelines & Engineering Support

• Work with senior engineers to document ETL flows and implement workflow automations.
• Contribute to data quality checks (DI/DQM) and metadata documentation.

4. Business Intelligence & Automation

• Support end-to-end workflow automation for analyticsalerting, reporting, and monitoring flows.
• Prepare storyboards and insight summaries for leadership and customers.

Expected Hard Skills

• Python

  (Pandas, NumPy, basic ML, automation scripts)

• SQL

  (joins, aggregations, window functions)

• Statistics

  (hypothesis testing, distributions, correlation, regression)

• Data Visualization

  (Power BI, Tableau, Matplotlib/Plotly)
• Understanding of data cleaning, preprocessing, feature engineering
• Basic knowledge in machine learning is a plus

Expected Soft Skills

• Proactive & self-starter mindset

• Strong

  communication

  skills
• Ability for

  storyboarding

  insights and presenting structured outputs

• Curiosity and hunger to learn

• Ability to go the

  extra mile

  and work in ambiguous fast-paced environments
• High ownership and strong attention to detail

Learning & Exposure You Will Get

During this internship, you will work directly with product, engineering, and data science leadership and gain exposure to:

• IoT, telematics & sensor data (GPS, OBD, fuel, video)

• SaaS product engineering & analytics workflows

• AI, ML & GenAI applications

  in real-world fleet & logistics

• Data engineering

  , pipeline development & end-to-end DI/DQM

• Optimization models

  and predictive analytics

• Workflow automation frameworks

• How modern

  fleet,

  logistics

  & transportation

  ecosystems operate
• Working in a

  high-growth, fast-paced startup

  environment

Who Should Apply

• Students or recent graduates in engineering, statistics, mathematics, computer science, or related fields
• Individuals passionate about AI, data science, IoT, transportation analytics, and solving real-world industry challenges
• People who want hands-on experience in building practical, scalable data products