Job Summary: We are looking for a Backtesting Leader who is both a strategist and a hands-on contributor—a true player-coach in the field of financial markets and trading strategy backtesting. The ideal candidate will lead , mentor , and actively participate in developing backtesting frameworks, ensuring high-performance execution and accuracy in trading models. Key Responsibilities: Lead and actively contribute to the design and execution of backtesting frameworks. Analyze historical market data and refine trading algorithms based on real-world simulations. Mentor and guide junior analysts and developers, fostering technical growth. Collaborate across teams with traders, quants, and software engineers to optimize trading strategies. Ensure compliance with regulatory and industry standards in trading strategy validation. Solve complex technical challenges , ensuring robustness in backtesting methodologies. Stay ahead of emerging trends in algorithmic trading and backtesting technologies. Qualifications: Education: Bachelor’s or Master’s degree in Finance, Economics, Mathematics, Computer Science, or a related field. Experience: 6+ years in backtesting, quantitative finance, and trading strategy development. Technical Skills: Expertise in Python or C++ . Strong knowledge of financial market data sources and APIs . Experience with backtesting platforms (TradingView, QuantConnect). Advanced proficiency in data science libraries (Pandas, NumPy). Leadership & Coaching Skills: Proven experience as a player-coach , leading teams while actively contributing to projects. Ability to mentor and train junior professionals in best practices. Strong communication and stakeholder management skills. Show more Show less
NuFinTech is building the next generation of AI-powered financial intelligence systems — from predictive analytics to market microstructure modeling to institutional-grade trading insights. We are looking for exceptional fresh graduates in AI/ML who want to develop real-world, high-impact systems used in modern finance. This is a 3-month paid internship with a guaranteed full-time job offer upon successful performance. What We’re Looking For We aren’t looking for ordinary ML practitioners — we want builders , researchers , and problem-solvers . You are a strong fit if you are: Kaggle-Caliber Talent Expert-level skills demonstrated by Kaggle competitions, notebooks, or ML pipelines. Ability to clean, engineer, and synthesize complex, messy datasets. Experience with model stacking/ensembles, feature engineering, and experimental design. AI Fluency in the Financial Sector (You don’t need professional experience, but must be deeply knowledgeable.) You should understand: Market prediction tasks (classification/regression, forecasting, volatility modeling) Options Greeks, volatility modeling, order flow, microstructure, etc. (big advantage) How to evaluate models with financial metrics (Sharpe, drawdown, expectancy) Handling non-stationary data, regime shifts, and walk-forward testing Strong Engineering Skills Python (NumPy, Pandas, Scikit-Learn, PyTorch/TF/JAX) Experiment tracking (MLflow, Weights & Biases) Writing clean, modular research code Responsibilities During the 3-month internship you will: AI Research Build predictive ML models for equities, options, and market microstructure. Develop novel features (e.g., volatility signals, flow metrics). Create ensemble models combining classifiers, regressors, and time-series architectures. Implement backtesting and simulation frameworks for predictive signals. Data Engineering Work with large, noisy, real-time financial datasets. Automate data pipelines, cleaning, normalization, and labeling. Experiment with feature extraction, including deep learning embeddings. Quant Insights & Reporting Analyze model performance across regimes and market conditions. Present weekly research findings and model improvements. Generate explainability reports (SHAP, ICE plots, feature attributions). What You Will Learn Professional quant research methodology Walk-forward validation & avoiding look-ahead bias How institutions use AI for market prediction Real-time model deployment Building production-ready financial AI systems Advanced feature engineering from financial microstructure Requirements Mandatory Bachelor’s degree in CS, Data Science, Math, Engineering, or related field Kaggle experience (notebooks, competitions, or pipelines) Strong Python + ML fundamentals Knowledge of financial markets & AI applications in finance Bonus Skills Experience with deep learning for tabular/time-series data Understanding of derivatives (gamma, vanna, skew, etc.) Quant research exposure through university projects or competitions 💼 What You Get Paid 3-month internship Direct mentorship from senior AI/quant leaders Real, production-level financial datasets Weekly research sprints State-of-the-art tools & GPU compute Full-time job opportunity upon successful completion (We hire every intern who meets performance standards — high performers are fast-tracked.)