Job Summary
Startup Inno is seeking a highly analytical and driven Quantitative Data Scientist – Algorithmic Trading to join our advanced trading and analytics team. This role is ideal for a professional who thrives at the intersection of data science, finance, and technology. You will be responsible for designing, building, and optimizing quantitative models that drive algorithmic trading strategies across multiple asset classes.
As a core member of our quantitative research group, you will work closely with traders, engineers, and business leaders to transform large-scale financial data into profitable, scalable, and risk‑aware trading systems. This is a high‑impact role where your insights will directly influence investment decisions and portfolio performance.
Key Responsibilities
Design, develop, and implement quantitative models for algorithmic trading strategies.
Conduct research on market microstructure, price dynamics, and statistical arbitrage opportunities.
Analyze large volumes of structured and unstructured financial data.
Build predictive models using machine learning and statistical techniques.
Backtest and validate trading strategies using historical and real‑time market data.
Optimize models for performance, robustness, and risk management.
Collaborate with software engineers to deploy models into production trading systems.
Monitor live trading strategies and continuously improve model accuracy and efficiency.
Document research findings and present insights to stakeholders.
Required Skills and Qualifications
Strong proficiency in Python, R, or C++ for quantitative modeling.
Advanced knowledge of statistics, probability, and linear algebra.
Hands‑on experience with machine learning algorithms (e.g., regression, clustering, deep learning).
Solid understanding of financial markets and trading instruments (equities, FX, crypto, derivatives).
Experience with time series analysis and forecasting models.
Familiarity with SQL, Pandas, NumPy, Scikit‑learn, TensorFlow/PyTorch.
Ability to work with large datasets and high‑frequency data.
Strong analytical thinking and problem‑solving skills.
Experience
Bachelors or Masters degree in Data Science, Quantitative Finance, Mathematics, Statistics, Computer Science, or related field.
2–6 years of experience in quantitative research, algorithmic trading, or financial data science.
Prior experience in fintech, hedge funds, investment banks, or trading firms is highly desirable.
Fresh PhD graduates with strong research background are also encouraged to apply.
Working Hours
Full‑time position (40 hours per week).
Flexible working hours with hybrid or remote options.
May require overlap with global market hours depending on projects.
Knowledge, Skills, and Abilities
Deep understanding of financial modeling and risk metrics.
Ability to translate complex mathematical concepts into practical solutions.
Strong communication skills to explain technical insights to non‑technical stakeholders.
High attention to detail and accuracy.
Ability to thrive in a fast‑paced, data‑driven environment.
Strong curiosity and passion for financial innovation and AI.
Benefits
Competitive salary with performance‑based bonuses.
Equity/stock options in a fast‑growing startup.
Flexible work arrangements (remote/hybrid).
Health insurance and wellness programs.
Learning and development budget for certifications and conferences.
Access to cutting‑edge tools and financial data platforms.
Collaborative and innovative work culture.
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