Data Scientist

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. 1.Email 

   Smriti5991@gmail.com

    with subject line 

   “Data Scientist — Your Name”.

2. 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. 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.