Quantitative Analyst

Location: Ahmedabad (on-site)

Type: Full time

Experience: 1-5 years

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About the Role

We are a proprietary quantitative trading firm focused on Indian and global equity, derivatives, and cryptocurrency markets. This role is responsible for researching trading ideas, analysing market data and developing systematic strategies, with scope to investigate other markets, instruments and asset classes where relevant.

You will take research from an initial observation through to a conclusion: forming a hypothesis about market behaviour, preparing the data required to test it, constructing signals and rules, building and running the backtest,

and determining whether the result holds up under further scrutiny. You will work closely with the team responsible for data and trading systems to specify what your research needs and to help suitable strategies progress towards live implementation.

Python is central to this role. You will write your own research and backtesting code and must be able to inspect and debug it yourself. Meaningful knowledge of at least one market - Indian, US or cryptocurrency - is required, along with the ability to investigate unfamiliar markets and instruments carefully. This is not an entry-level position.

Responsibilities

- Develop and test research ideas. Form research questions from market observations, economic reasoning,

statistical patterns or observed strategy behaviour. Be able to explain why an effect might exist, and what would have to be true for it to persist, before committing to testing it.
- Design strategies end to end. Define instruments, universe, observation frequency, signals, entry and exit conditions, filters, holding period, position sizing and risk controls. Explain the reasoning behind the universe,

including instruments deliberately excluded and why.
- Investigate market and instrument mechanics. Understand how trading sessions, liquidity, contract specifications, costs, order behaviour and execution constraints affect whether a strategy is feasible in practice rather than only on paper.
- Write research and backtesting code in Python. Prepare data, run exploratory analysis, calculate signals,

implement backtests and evaluate results, working extensively in pandas and NumPy. Write, inspect and debug this code independently.
- Work carefully with financial time-series data. Handle identifiers, timestamps, timezones, differing frequencies and datasets from multiple sources. Investigate missing observations, inconsistent prices,



corporate-action adjustments and other data issues before relying on any result.
- Evaluate results critically. Examine performance across time periods, instruments and market conditions.

Investigate drawdowns, turnover, concentration, trade counts, parameter sensitivity and the effect of realistic trading costs and assumptions. Distinguish a genuine effect from one produced by the testing process itself.
- Keep exploration separate from evaluation. Reserve data that has not been used to select the model or its parameters, and be able to state what a given result does and does not demonstrate.
- Take promising strategies towards implementation. Specify data requirements, validate signals and calculations against the production data layer, and support the work required to move a strategy into the live framework.
- Analyse live and paper performance. Where a strategy has been deployed, compare its behaviour against the backtest and help establish whether differences arise from data issues, implementation differences, execution effects or a change in the underlying opportunity.
- Maintain clear records of your research. Keep hypotheses, datasets used, code, results, attempted variations and rejected approaches organised, so that conclusions can be reproduced and re-examined.

What We Expect

- Relevant professional experience in quantitative research, systematic trading, strategy analysis or closely related market work. You should already have built and evaluated strategies of your own.
- Practical understanding of a financial market. Indian, US or cryptocurrency. You should understand how the market you have worked in actually functions - instruments, sessions, liquidity, costs, order behaviour - and what the available data does and does not represent.
- Strong hands-on Python ability. Particularly pandas and NumPy. You should be comfortable writing research code from scratch, working with time-series and panel data, and handling messy real-world datasets without depending on a framework to structure the work for you.
- Working knowledge of statistics and probability. Returns and distributions, sampling uncertainty,



correlation and its limitations, regression, hypothesis testing, stationarity, sample size, and the common ways in which a result can be fitted rather than real.
- Experience cleaning and analysing real financial datasets, with attention to timestamp alignment, data availability, identifier consistency and historical accuracy.
- The ability to explain a strategy you have built in complete detail - what it captures, why you expected it to work, what it assumes, how it was tested, where it performs poorly and what remains unresolved.
- Sound judgment about what to do next. Ability to discuss failures and limitations directly, identify which additional checks matter, and decide which experiments are worth running.
- Ability to communicate clearly and work with colleagues on data, validation and implementation questions.
- Comfort working on Linux, reading and writing SQL, and working with large datasets.

Preferred Experience

- Taking a researched strategy through paper trading or live implementation, and investigating the differences from its backtest.
- Experience across more than one market, and the ability to compare how an idea behaves in different market structures.
- SQL and direct experience with data held in PostgreSQL, ClickHouse, Parquet or CSV.
- Obtaining or validating data through REST APIs or WebSocket feeds.
- Building a backtesting framework from scratch, rather than only using an existing one.
- Derivatives and options, cryptocurrency spot and perpetual instruments, market microstructure, or execution and transaction-cost analysis.
- More advanced statistical or machine-learning methods, where you can explain why the method suited the research problem.

What This Role Is Not

- A reporting or business-intelligence role. Dashboards, MIS, visualisation tools and Excel-based analysis are not the deliverables.
- A discretionary trading role. This is systematic research. Trading decisions based primarily on personal judgment, manual chart reading or reacting discretionarily to news are not the work.
- A sales, advisory or client-facing role. The position does not involve fundraising, client acquisition or providing investment advice.
- A role that ends at the notebook. Research is expected to be reproducible, examined properly and taken far enough that a conclusion can be relied upon.
- A tool-driven role. Research is implemented in Python. Platform-based or GUI backtesters may appear in your background, but they are not a substitute for writing the analysis yourself.

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