Quantitative Financial ResearcherArjuna Space AI | Pune / Hybrid

Engineering geospatial intelligence for India's financial markets.

About Arjuna

Arjuna Space AI is building geospatial intelligence infrastructure for financial markets by transforming satellite, geospatial, weather, maritime, infrastructure and other real-world data into proprietary financial signals.

Financial markets have abundant information about prices and companies but much of the physical economy remains difficult to measure continuously.

Arjuna's mission is to make that physical economy measurable.

We are looking for a highly analytical Quantitative Financial Researcher to help us turn alternative data into signals that institutional investors can actually use.

This is an early-stage, high-ownership role. You will work directly with the founding team to define what we should measure, build the research methodology, test whether signals have economic value, and help turn successful research into products.

What you will do1. Discover financial signals

Identify areas where alternative data can provide information that traditional financial datasets cannot easily capture.

Examples

- Infrastructure construction activity
- Industrial expansion
- Port and shipping activity
- Energy infrastructure deployment
- Agricultural activity
- Commodity flows
- Physical climate risk
- Supply-chain activity

Your question will constantly be: “What can we observe in the physical world before—or differently from—the financial markets?” 2. Build quantitative research frameworks

Develop methodologies to transform raw observations into measurable signals.

You will work with

- Time-series data
- Satellite-derived features
- Geospatial datasets
- Market prices
- Financial statements
- Macroeconomic data
- Weather data
- AIS/maritime data
- Alternative datasets

You will design:
- Features
- Indicators
- Scores
- Indices
- Event signals
- Forecasting models
- Historical benchmarks

1. Backtest signals

Determine whether an Arjuna signal actually contains useful information.

You will

- Construct historical datasets
- Define hypotheses
- Perform statistical tests
- Backtest signals
- Measure predictive power
- Analyze lead/lag relationships
- Test robustness across time periods and sectors
- Identify false positives and false negatives
- Evaluate transaction costs and practical implementability where relevant

We care much more about rigorous research than impressive-looking correlations.

1. Connect physical signals to financial outcomes

A satellite observation by itself isn't necessarily valuable. You will investigate whether observations correlate with or predict:
- Revenue
- Production
- Capacity




- Commodity prices
- Earnings surprises
- Asset utilization
- Sector performance
- Volatility
- Risk
- Other economically meaningful outcomes

Your job is to bridge: Physical observation → economic activity → financial implication

1. Work with the AI/geospatial engineering team

You will collaborate closely with:
- Geospatial ML engineers
- Data engineers
- AI researchers
- Product engineers

You will translate financial research questions into technical requirements.

For example

“Can we detect construction acceleration at these 500 industrial facilities and generate a monthly activity index?”

You will help define what constitutes a meaningful signal, while the engineering team builds the underlying detection system.

1. Build institutional-grade research

Create research that can withstand scrutiny from sophisticated investors.

You will produce

- Research reports
- Signal methodologies
- Backtesting frameworks
- Data-quality assessments
- Research dashboards
- Investment-relevant insights
- Technical documentation

Eventually, you will help build the methodology behind Arjuna Intelligence , our institutional intelligence platform. What we're looking forMust-have

- Strong quantitative background in finance, economics, mathematics, statistics, engineering, physics, computer science or a related field
- Strong Python skills
- Excellent understanding of statistics and probability
- Experience working with financial/time-series data
- Ability to independently formulate and test hypotheses
- Strong understanding of financial markets
- Ability to distinguish genuine predictive signals from spurious correlations
- Excellent analytical and problem-solving ability
- Strong written communication

Strongly preferred Experience with one or more of:
- Quantitative research
- Systematic investing
- Alternative data
- Equity research
- Portfolio analytics
- Factor research
- Algorithmic trading
- Financial modelling
- Econometrics
- Machine learning
- Geospatial analytics
- Satellite data
- Commodity markets
- Infrastructure/energy research

Experience with Indian financial markets is a major plus.

Technical skills

You should be comfortable with:

Python

- pandas
- NumPy
- SciPy
- scikit-learn
- statsmodels
- matplotlib

And ideally some exposure to:
- SQL




- Jupyter
- Git
- APIs
- cloud data platforms
- geospatial data
- time-series databases

Experience with tools such as GeoPandas, rasterio, xarray or Google Earth Engine is a significant advantage, but we care more about quantitative thinking than a particular software stack.

What success looks like

Within your first 6 months, you should be able to take an idea from:

“Could satellite data tell us something useful about Indian infrastructure?”

to:

Hypothesis → Dataset → Feature → Signal → Backtest → Validation → Financial interpretation → Product

You will be successful if you help Arjuna discover signals that institutional investors genuinely care about and cannot easily obtain elsewhere.

The kind of person we want

You are probably a good fit if you:

- Get obsessed with finding patterns in messy data
- Question your own hypotheses
- Enjoy building things from scratch
- Can move between finance, statistics and technology
- Don't need a predefined research problem
- Are comfortable saying “the data doesn't support our hypothesis”
- Care about understanding why a signal works, not just whether it backtests
- Want to work on a problem that sits between technology and financial markets

You should be comfortable in an environment where the research agenda is still being created. There is no textbook for what we're building.

Why join Arjuna?

You won't be joining an established quant fund to maintain someone else's models.

You will help define an entirely recent category:

Physical-world intelligence for financial markets.

You will work at the intersection of:

Quantitative Finance × AI × Satellite Intelligence × Geospatial Data × Alternative Data

Your research could become a financial signal used by institutional investors or become the foundation of an entirely new Arjuna product.

You will have significant ownership and direct access to the founding team.

Location

Pune, India — Hybrid

We are open to exceptional candidates elsewhere in India depending on the role and level.

Experience

2–6 years preferred , but exceptional candidates with less experience and outstanding quantitative research ability are encouraged to apply.

Compensation

Competitive startup compensation with meaningful ownership potential for exceptional candidates.

The question we want you to help us answer Can we observe India's physical economy early enough and accurately enough to create information advantage for financial markets?

If that question excites you, we want to hear from you.

Arjuna Space AI

Engineering geospatial intelligence for India's financial markets.

📌 Quantitative Financial Researcher (Pune)
🏢 Arjuna Space AI India
📍 Pune