This is a full-time, remote Quantitative Analyst position paying $135,000 per year. The role sits on the analytics side of a finance-focused team, building the models that inform investment and risk decisions rather than executing trades directly.
The work is closer to research than to a trading floor. Models are built, tested against real market history, and handed off with enough documentation that someone else could rebuild the logic from scratch if needed. Speed matters less than getting the underlying math right.
Pay and benefits
Base pay is $135,000 annually in USD. On top of that:
- Health coverage
- Paid time off
- 401(k) matching
- Performance-based bonuses
- Stock options or profit-sharing, depending on the specific compensation package offered at time of hire
Quantitative analyst remote salary figures at this level often include a meaningful bonus component tied to model performance or team results, and this role follows that same structure rather than a flat salary alone.
Education and experience
A master's degree is the minimum requirement, in mathematics, statistics, finance, or another quantitative field. Along with the degree, candidates need at least 3 years of experience in financial or risk analytics.
The degree requirement here is firm. This isn't a role where a strong portfolio can substitute for graduate coursework, mostly because the modeling work draws on statistical theory that's difficult to pick up entirely outside a formal program.
The three years of experience should be specifically in financial or risk analytics, not in general data science or academic research alone. A candidate with a pure research background can still be a strong fit, but the resume needs to show direct exposure to how models are used in an actual investment or risk context, not just how they perform in a paper or classroom setting.
What the role covers
- Build statistical and financial models used to support investment or risk decisions.
- Backtest trading or risk strategies against historical data before they go anywhere near live use.
- Present analytical findings and model results to stakeholders who need to sign off on them.
- Maintain and refine existing models as market conditions or data availability shifts.
- Document modeling assumptions clearly enough that another analyst could pick up the work later.
A typical project might start with a hypothesis about how a particular asset class behaves under stress, get tested against a decade of historical data, and end with a short presentation to a risk committee explaining whether the model holds up or needs more work. That cycle- hypothesis, testing, presentation- repeats often enough that it shapes most of the calendar.
Not every project ends with a clean result. Sometimes the backtest reveals that a model performs well in calm markets but falls apart under stress, which is itself a useful finding worth presenting, even if it means the model doesn't move forward as planned.
Skills
Programming ability in Python or R is expected, along with solid statistical modeling and financial mathematics. General data analysis and risk modeling round out the core set.
Beyond the required list, familiarity with SQL for pulling data directly, exposure to a backtesting framework like Backtrader or QuantConnect, and any prior work with time-series forecasting all count in a candidate's favor.
None of the bonus skills are dealbreakers on their own. A candidate strong on the core requirements but missing all three nice-to-haves is still a reasonable fit, since those skills tend to develop quickly once someone is working with the team's specific data pipeline and tools.
How the work is structured
Work is done remotely, and the team is spread across a few time zones, so most collaboration occurs through written documentation and scheduled reviews rather than constant real-time back-and-forth. That said, model reviews with stakeholders do need to happen live, so some meeting overlap with the core team is necessary.
A typical week includes a mix of independent modeling time and at least one scheduled review, either with the broader analytics team or with the stakeholders who'll act on a model's output. The independent stretches give room to work through a problem without interruption, which matters for the kind of statistical work this role involves.
Deadlines cluster around quarterly reviews and specific model rollouts, and Naukri Mitra client teams in finance analytics tend to structure their calendars similarly. Outside those windows, the schedule has more room for exploratory work, testing ideas that may or may not become a usable model.
Career path
People who move into remote quantitative analyst jobs from adjacent fields, actuarial work, academic research, or a data science role with a strong statistics foundation find the transition manageable once the finance-specific modeling knowledge is in place. For someone coming from one of those backgrounds, domain knowledge in finance tends to be the main gap in learning to become a remote quantitative analyst, more so than the technical or programming side.
Growth from here typically moves toward either a senior modeling role with greater ownership of strategy design, or a path into a more specialized area such as derivatives pricing or credit risk. Both are realistic outcomes for someone who spends a few years building a solid track record in this seat.
The specialization path appeals most to people who found one particular type of model more interesting than the rest of the work during their first year or two. There's no set timeline for making that shift, and plenty of analysts stay generalists for their whole career instead, moving across asset classes and model types rather than narrowing in on one.
Applying
A resume and a sample of prior modeling work, code, a write-up, or a past project summary will move an application along faster than a resume alone. This is a fully remote opening among quantitative analyst jobs worldwide, and location isn't a barrier as long as working hours allow for reasonable overlap with the team.
Applications are reviewed as they come in rather than held for a single batch date. A brief note on which part of the modeling process interests you most- risk, pricing, or strategy testing- can help the team route your application to the right reviewer faster.
