Location : Hyderabad (preferred) | Hybrid-friendly About InvestorAI InvestorAI is an AI-first fintech company that turns complex market data into real-time investment insights for global investors. We use cutting-edge AI techniques like deep learning, genetic algorithms, and intelligent data pipelines to build models that trade in live markets. Now, we're looking for curious, motivated junior data scientists who are eager to learn, grow, and help shape the future of AI in finance. Why This Role is Exciting Real-world Impact : Your work will contribute to models that power real-time trading decisions. Learning Environment : Get hands-on experience with machine learning in a live production setting. Innovative Projects : Learn about state-of-the-art techniques like CNNs, Transformers, and time-series analysis. What You’ll Do Explore financial data to identify useful patterns and features. Support model development using Python and ML frameworks like TensorFlow or PyTorch. Assist in testing and deploying models into a live trading system. Monitor model performance and help troubleshoot issues. Collaborate with senior data scientists, engineers, and finance professionals. What We’re Looking For 0–1 years of experience in machine learning or data science (internships/projects count). Strong programming skills in Python . Familiarity with machine learning concepts and libraries (e.g., scikit-learn, PyTorch, or TensorFlow). Good understanding of statistics and time-series data. Good communication skills and eagerness to learn. Bachelor’s degree in Computer Science (preferably pwith Data Science specialization) Nice-to-Haves (Not Required, But a Plus) Experience with version control (Git), SQL, or Jupyter notebooks. Exposure to financial markets or interest in investing/trading. Participation in Kaggle competitions, open-source projects, or personal ML projects.
Data Scientist — Quantitative Machine Learning Location Hyderabad (preferred) — hybrid friendly. Exceptional remote candidates within India will be considered. About InvestorAi InvestorAi is an AI-first fintech that transforms vast, noisy market data into real-time, alpha-generating insights for global investors. Our proprietary “spatial-ordering” pipelines, genetic-algorithm search, and deep-learning mixture-of-experts have delivered a 45 % CAGR while remaining lean and profitable. With new capital and an ambitious roadmap, we’re scaling research and production deployment of models that trade live capital every day. Our Al algorithms have been trained and developed by our expert tam at Bridgeweave Labs using 14 years of stock market data. We perform over 35 million computations using sophisticated AI techniques like Computer Vision, Convolutional Neural Networks, Genetic Algorithms etc. to produce the best possible investment ideas. We have over 2 years of track record of producing exceptional results and outlier win rates for our investors. Why this role matters Model ownership, end-to-end: You’ll design, train, validate, and deploy production-grade models that directly drive portfolio decisions and P&L. Cutting-edge R&D: Work on entropy-based initializers, hybrid CNN/Transformer architectures, and Optuna-driven hyper-parameter searches—ideas that rarely escape academia. Massive impact: A single improvement in signal quality or latency can unlock millions in incremental alpha. Key Responsibilities Area What You’ll Own Research & Signal Discovery Ideate and prototype novel features from tick-level, fundamental, and alternative data; explore quantum-inspired ordering, autoencoders, time-series augmentations. Model Development Build, iterate, and benchmark CNNs, Transformers, GNNs, and Mixture-of-Experts using TensorFlow/PyTorch ; run large-scale Optuna sweeps on our GPU cluster. Deployment & MLOps Package models as Docker images, publish to our Kubernetes-based inference platform, and write CI/CD tests to ensure reproducible builds and seamless rollbacks. Performance Monitoring Create dashboards for live AUC-ROC, precision-recall, drawdown, beta, and slippage; set alert thresholds and conduct post-mortems when KPIs drift. Cross-functional Collaboration Partner with quant researchers, portfolio managers, and full-stack engineers to push models from Jupyter notebooks into live trading strategies. Documentation & Knowledge Sharing Draft technical memos, run code-reviews, and mentor junior analysts on best practices in robust ML for finance. Must-Have Qualifications 3 – 6 years building and shipping ML models in production (preferably finance, ad-tech, or other latency-sensitive domains). Expert-level Python plus strong grasp of TensorFlow or PyTorch ; comfortable profiling GPU memory and optimizing kernels. Proven experience with model deployment (Docker, Kubernetes, CI/CD, feature stores, model registries). Solid understanding of statistics, probability, and time-series analysis; able to articulate bias-variance trade-offs and back-test pitfalls. Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; a Master’s degree is preferred. Hands-on with SQL and at least one distributed data tool (Spark, Dask, or Ray). Clear, concise communicator—capable of translating technical findings into investor-friendly language. Nice-to-Have Extras Familiarity with trading venues, order-book dynamics, and transaction-cost analysis. Contributions to open-source ML libraries or finance-related GitHub projects. Experience with Optuna, genetic algorithms, or Bayesian optimization frameworks. Knowledge of Von Neumann entropy, quantum-inspired computing, or information-theoretic model selection. Comfort working in a start-up-style, high-ownership culture (you build it, you run it) What We Offer Work with best brains in Data Science and cutting edge modelling to solve complex finance problems Competitive salary + performance bonus tied to desk P&L. ESOP after probation. Comprehensive health insurance (self & family). Annual professional-development budget (courses, conferences, journals). 18 days paid vacation + flexible wellness leave. How to Apply 1.Email Smriti5991@gmail.com with subject line “Data Scientist — Your Name”. 2.Attach your CV (≤ 2 pages) and link to a GitHub repo or notebook showcasing a project you took all the way to deployment . 3.Short-listed candidates complete a programming test (feature engineering, model training, deployment script) followed by two technical interviews and a culture chat with the founders. We hire for curiosity, rigor, and ownership. If you’re excited by the idea of shipping ML that lives (and profits) in the wild, we’d love to meet you.