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Nagpur, Maharashtra, INPosted April 29, 2026

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Job Description

Job Title: Data Scientist – Level 1

Location: New Delhi

Employment Type: Full-Time (Work From Office)

Experience: Minimum 2 Years

About Jarvis Technology & Strategy Consulting:

Jarvis Technology & Strategy Consulting is one of India’s leading political and policy consulting organizations, founded by socio-entrepreneurs from premier institutions such as IIT, ISB, and NIT. With a strong presence across the country, we leverage data and technology to drive large-scale political campaigns, design action-oriented community outreach programs, enable targeted communications, and improve the effectiveness of government initiatives.

Role Overview:

We are looking for a Data Scientist (Level 1) to work on real-world, large-scale datasets and contribute to high-impact projects in the political and governance space. In this role, you will be responsible for analyzing data, building models, and generating insights that directly influence strategic decisions.

This role is ideal for professionals with a strong analytical foundation who are eager to take ownership and work in a fast-paced, impact-driven environment.

Key Responsibilities:

  • Rapid Prototyping : Develop quick-turnaround analytical proofs-of-concept (PoCs) to validate hypotheses before committing to full-scale model development.
  • Take full ownership of the data pipeline, from initial data cleaning and exploratory analysis to model deployment and final presentation.
  • Cross-Functional Translation : Distill complex statistical outputs into clear, non-technical narratives for stakeholders in Operations and Research.
  • Data Quality Auditing : Implement rigorous validation checks to ensure accuracy in large-scale datasets, as decisions are based on high-integrity outputs.
  • Analyze structured and unstructured datasets to derive actionable insights.
  • Build, evaluate, and optimize statistical and machine learning models.
  • Design and develop dashboards, reports, and data visualization frameworks.
  • Translate business and operational problems into data-driven solutions.
  • Present insights and recommendations to stakeholders in a clear and structured manner.
  • Stay updated with latest tools, technologies, and best practices in data science.

Qualifications

  • Bachelor’s/Master’s degree in Engineering, Computer Science, Statistics, Mathematics, or a related field.
  • Minimum 2 years of relevant experience in Data Science / Data Analytics roles.
  • Strong proficiency in Python (Pandas, NumPy; experience with ML libraries preferred).
  • Ability to work independently in a fast-paced environment with ownership.

Good to Have:

  • First-Principles Thinking : A strong problem-solving mindset that breaks down vague business problems into structured data tasks.
  • Cognitive Flexibility : Ability to pivot between multiple projects and adapt to changing priorities in a high-intensity environment.
  • Advanced SQL & Schema Design : Move beyond simple extraction to include experience with complex joins, window functions, and optimizing queries for large datasets.
  • Unstructured Data Processing : Familiarity with techniques to handle "messy" data, such as text mining, string matching, or fuzzy logic.
  • Experimental Design : Understanding of A/B testing or randomized control trial (RCT) logic to measure the impact of specific interventions.
  • Exposure to predictive modeling, clustering, or classification techniques.
  • Understanding of political, governance, or public sector data (preferred).

Why Join Jarvis:

  • Work on high-impact, real-world problems at scale.
  • Exposure to large datasets and complex decision-making scenarios.
  • Opportunity to work closely with experienced data and tech teams.
  • Fast-paced environment with high ownership and learning.
  • Direct contribution to projects in governance and public systems.

Requirements

  • Available for full-time, in-office role in New Delhi.
  • Willingness to work in a dynamic and high-impact environment.
  • Immediate joiners preferred but not necessary.

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