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Applications open Full-Time Job

Python Developer

Tower Research Capital

  • Gurugram, India
  • Competitive Package
  • 3-5 Years
  • Posted Yesterday
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About the role

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization.

Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

Responsibilities

  • Working on team’s core python data science platform to support global trading research
  • Building ETL pipelines on top of internal and external data sources to drive business analytics
  • Researching solutions to allow increasingly sophisticated visualizations
  • Developing / improving frameworks to better leverage our cloud + on-prem compute and storage clusters
  • Developing, testing and documenting existing in-house tools to improve their quality, reliability and supportability
  • Automating existing support workflows to reduce operational burden and design new tools to improve efficiency

Qualifications

The ideal candidate will have

  • A bachelor’s degree in computer science from a top-tier college or university
  • 3 to 5 years of relevant experience in designing and improving information systems.
  • Proficiency with Python and knowledge of NumPy, Pandas/Polars, Cython and other python data science frameworks
  • Thorough understanding of Linux fundamentals and C++ compiling/linking/loading process.
  • Experience with Source Control Management, CI/CD pipelines, ELK and other observability frameworks will be an added advantage
  • Brilliant problem-solving abilities. Most problems require high-availability, high-throughput and low latency solutions.

Benefits

Tower’s headquarters are in the historic Equitable Building, right in the heart of NYC’s Financial District and our impact is global, with over a dozen offices around the world.

At Tower, we believe work should be both challenging and enjoyable. That is why we foster a culture where smart, driven people thrive – without the egos. Our open concept workplace, casual dress code, and well-stocked kitchens reflect the value we place on a friendly, collaborative environment where everyone is respected, and great ideas win.

Our benefits include

  • Generous paid time off policies
  • Savings plans and other financial wellness tools available in each region
  • Hybrid working opportunities
  • Free breakfast, lunch, and snacks daily
  • In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
  • Company-sponsored sports teams and fitness events (JPM Corporate Challenge, Cycle for Survival, Wall Street Rides FAR and more)
  • Volunteer opportunities and charitable giving
  • Social events, happy hours, treats, and celebrations throughout the year
  • Workshops and continuous learning opportunities

At Tower, you’ll find a collaborative and welcoming culture, a diverse team and a workplace that values both performance and enjoyment. No unnecessary hierarchy. No ego. Just great people doing great work – together.

Tower Research Capital is an equal opportunity employer.

Eligibility & process

Who can apply

Employment type: Full-time

Skills & stack

Python CI/CD Linux Machine Learning Django FastAPI Docker REST APIs

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, CI/CD, Linux, Machine Learning, Django, FastAPI, Docker, REST APIs) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Tower Research Capital Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Tower Research Capital as a Python Developer?
Highlight Tower Research Capital's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Tower Research Capital Competitive Package
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