Financial Engineer

Gemini Solutions Pvt Ltd; Los Angeles (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Gemini Solutions Pvt Ltd in Los Angeles seeks a Financial Engineer to join the team at the intersection of quantitative analytics, risk management and technology. You will work with quant, risk, portfolio and engineering teams to build solutions, wrappers and integrations around quantitative models and analytics.

The ideal candidate combines strong markets and risk knowledge with hands-on programming in Python and SQL, and can explain results and differences in risk and attribution to business

Qualifications

• 5+ years in Financial Engineering, Quant Analytics, Risk Analytics or related roles.
• Strong Python and SQL; production‑quality analytical solutions.
• Strong understanding of Fixed Income and risk analytics.
• Ability to explain drivers behind risk/attribution changes to business stakeholders.

Responsibilities

• Work with Quant, Risk and Investment teams to understand models, analytics and requirements.
• Support Fixed Income and structured-product analytics, including pricing, risk and cash-flow analytics.
• Build wrappers, services and APIs around quantitative/risk models and analytics platforms.
• Analyze and explain changes in risk, performance and attribution results.
• Validate inputs/outputs for analytics models against historical data to ensure accuracy.
• Acquire, clean, and analyze large-scale financial datasets from multiple sources.
• Build data pipelines for real-time and batch processing of market data.
• Investigate breaks across positions, market data, cash flows, model inputs and analytics.
• Develop validation checks and support root-cause analysis.
• Collaborate with engineering teams to productionize quantitative solutions.

Skills

Python

SQL

Fixed Income

Risk Analytics

Quantitative Analytics

FES

Quant Development

Investment Analytics

Tools

QuantLib

NumPy

Pandas

SciPy

APIs

Financial data pipelines

Job description

We are seeking a Financial Engineer to work at the intersection of Quantitative Analytics,

Risk Management and Technology. The role will involve working closely with Quant teams,

Risk Managers, Portfolio Managers and Technology teams to build solutions, wrappers,

services and integrations around quantitative models and analytics.

The ideal candidate combines strong financial markets and risk knowledge with hands-on

programming and analytical skills, and is comfortable explaining quantitative results and

investigating differences in risk and attribution numbers.

Key Responsibilities:

• Work with Quant, Risk and Investment teams to understand quantitative models, analytics and business requirements.
• Support Fixed Income and structured-product analytics, including pricing, risk and cash-flow analytics.
• Build wrappers, services, APIs and integrations around quantitative/risk models and analytical platforms.
• Analyze and explain changes or differences in risk, performance and attribution results.
• Validate input and outputs for analytics models against historical and computed data to ensure accuracy and robustness.
• Acquire, clean, and analyze large-scale financial datasets from multiple sources.
• Build data pipelines for real-time and batch processing of market and reference data
• Investigate breaks across positions, market data, reference data, cash flows, model inputs, and analytics.
• Develop functional and statistical data/analytics validation checks and support root-cause analysis.
• Collaborate with engineering teams to productionize quantitative solutions and data pipelines.

Required Skills & Experience:

• 5+ years in Financial Engineering, Quantitative Analytics, Risk Analytics, FES,
• Quant Development or Investment Analytics.
• Strong experience working directly with Quant teams, Risk Managers and/or Portfolio Managers.
• Strong Python and SQL skills; experience building production-quality analytical solutions.
• Strong understanding of Fixed Income and risk analytics
• Ability to understand why risk or attribution numbers change and explain the drivers to business stakeholders.
• Understanding of the trade lifecycle through risk and attribution.

Strongly Preferred:

• Hands-on experience with Mortgage / MBS, ABS, Credit or other structured products.
• Experience with mortgage/ABS cash flows, prepayment, default and embedded optionality.
• Experience working in an FES / Quantitative Analytics environment supporting institutional investment or risk teams.

Knowledge of QuantLib, NumPy, Pandas, SciPy, APIs and financial data pipelines.