Research Engineer (Quantitative)

Research Engineer( Quantitative)

Who we are:

Progrid Analytics is an energy trading company based in San Francisco, California. We are a small team of technologists and statisticians working on energy trading strategies in the US market. We are looking for a data nerd who can do magic with numbers. This position is remote, based out of India.

The interview process involves a resume shortlist and will have two rounds of technical and fit interviews with our team. We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin or disability.

About the position:

Role: Research Engineer (Quantitative)

Location: India - Remote

Timings: Full-time (minor overlap is expected with Central time US)

What you’ll do:

• Design and implement statistical experiments to validate hypotheses on multifaceted datasets
• Build and optimize predictive models (classification, regression, time-series forecasting) to inform trading decisions
• Implement on end-to-end data science pipelines: data ingestion, cleaning, feature engineering, model training, and evaluation
• Write clean, modular code and production-grade code to support research and production workflows
• Collaborate with a close-knit team to prototype, iterate on and deploy data-driven strategies
• Present findings and model performance to both technical and non-technical stakeholders

What you bring: (requirements)

• Master’s or Bachelor’s in Computer Science, Machine Learning, or Data Science
• Strong programming skills in Python, with practical experience leveraging tools for numerical computing, data manipulation, and applying machine learning techniques in real-world workflows.
• Demonstrated experience building and maintaining robust, data-intensive ETL pipelines.
• Proven track record of writing clean, modular, production-ready code, with familiarity in testing, deployment pipelines, and version control.
• Excellent analytical and problem-solving skills for working with complex and noisy data.
• Ability to clearly communicate model insights and experimental findings to both technical and non-technical audiences.
• Collaborative and team-oriented approach, with experience delivering end-to-end data science solutions in small, cross-functional environments.
• Bonus: Experience working with time-series datasets, especially from the energy or finance sector