AI/ML Researcher - Quant & Financial Intelligence

Full Description

Job Description - AI/ML Researcher

Spark Talent is seeking a highly skilled AI/ML Researcher withhands-on experiencein applied machine learning, quantitative modelling, and financial prediction.

This role is ideal for individuals who have a strong research mindset, demonstrated expertise through Kaggle competitions, GitHub projects, and real-world ML experimentation.

We are seeking researchers who think creatively, conduct rigorous experiments, and are passionate about developing high-impact models for financial intelligence and quantitative analysis.

Key Responsibilities:

• Develop and optimize predictive ML models for equities, options, order flow, and market microstructure forecasting.

• Engineer advanced quantitative features such as volatility indicators, gamma exposure (GEX), vanna/charm signals, and flow-based metrics.

• Build, tune, and evaluate ensemble architectures across classifiers, regressors, and time-series models.

• Design robust backtesting and simulation frameworks to validate predictive signals and strategies.

• Work with large-scale, noisy, and real-time financial datasets, handling missing data, anomalies, and distributional shifts.

• Automate data pipelines for ingestion, cleaning, normalization, feature engineering, and labeling.

• Experiment with deep-learning-based feature extraction, embeddings, and hybrid modelling techniques.

• Analyse model performance across various market regimes, volatility environments, and structural shifts.

• Prepare clear research documentation, weekly updates, and insights on model enhancements and experiments.

• Build explainability reports using SHAP, LIME, ICE plots, or similar tools.

• Reproduce and implement quant/ML research papers and benchmark new modelling approaches.

Requirements:

• 1–2 years of hands-on experience in machine learning, data science, or quantitative research.

• Strong Kaggle profile (competitions, notebooks, or datasets) demonstrating advanced ML capabilities.

• Robust GitHub portfolio showcasing research projects, ML pipelines, model implementations, or financial experiments.

• Proven capability in handling large, complex, and noisy datasets, including feature engineering and data synthesis.

• Experience with model stacking, ensembling, experimental design, and optimization of ML workflows.

• Strong understanding of market prediction tasks such as classification, regression, time-series forecasting, and volatility modelling.

• Familiarity with quantitative finance concepts such as:

• Options Greeks (Delta, Gamma, Vanna, etc.)

• Volatility surfaces

• Order flow

• Market microstructure

• Knowledge of financial evaluation metrics (Sharpe ratio, drawdown, CAGR, expectancy).

• Experience working with non-stationary data, regime detection, and walk-forward validation.

• Proficiency in Python, including NumPy, Pandas, Scikit-Learn, PyTorch/TF/JAX.

• Experience with experiment tracking (MLflow, Weights & Biases).

• Ability to write clean, modular, research-grade code and conduct reproducible experiments.

Qualification: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical field.

Preferred Skills:

• Experience with deep learning for tabular and time-series models.

• Knowledge of derivatives concepts (gamma, vanna, skew, IV dynamics).

• Prior exposure to quantitative research, university competitions, academic papers, or open-source contributions.

• Experience implementing models from research papers or quantitative finance literature.

eSpark provides the following benefits:

• Flexible work environment

• Paid time off & annual leaves

Job Type: Full-time

Work Location: Remote