Quant Researcher

We are hiring high-caliber engineers with 3+ years of experience to build low-latency, high-throughput trading infrastructure in Rust, working directly under senior systems architects. This is a hands-on coding role on performance-critical systems — you will write, test, and ship the core code.

Responsibilities

1. Develop and maintain high-performance backend services in Rust under the guidance of senior engineers
2. Implement components of distributed, fault-tolerant systems handling high transaction volumes
3. Write extensive unit, property-based, and simulation tests for correctness-critical code - Profile and optimize code for latency, throughput, and memory efficiency - Participate in design discussions and rigorous code reviews
4. Debug and resolve issues in Linux-based production environments

Required Qualifications

1. B.Tech/M.Tech in Computer Science from an IIT or NIT (mandatory) - 3+ years of professional software development experience in Rust, C++, or Java/Go with systems-level work; strong Rust proficiency or demonstrated ability to attain it fast - Understanding of financial markets and trading systems — order books, order types, matching concepts, spot and derivatives (futures/options) basics; hands-on experience with trading, broking, or market data systems is a strong plus - Excellent computer science fundamentals — data structures, algorithms, operating systems, networking, concurrency
2. Solid grasp of multithreading and memory management; exposure to performance profiling and benchmarking
3. Understanding of distributed systems concepts — replication, consistency, message queues (Kafka/NATS or similar)
4. Experience with Linux, Git, and modern development workflows
5. Strong problem-solving ability and hunger to work on hard systems problems

Preferred

1. Competitive programming background (Codeforces/ICPC/CodeChef ratings welcome on the CV)
2. Personal or open-source Rust projects
3. Exposure to low-latency techniques — lock-free structures, zero-allocation design, binary protocols (SBE/Protobuf/FlatBuffers)
4. NISM certifications, trading experience (personal F&O/options trading counts), or coursework in financial engineering
5. Exposure to consensus algorithms (Raft), event sourcing, or deterministic testing

Growth Path

You will be mentored directly by senior systems engineers with deep exchange-infrastructure experience, with a defined progression to senior engineer ownership of subsystems within 18–24 months. This team is being built as a center of excellence in low-latency systems engineering in India.

Selection Process

1. Resume shortlisting (college + evidence of strong engineering: projects, CP ratings, internships)
2. Online DSA + systems assessment
3. Live coding round (Rust/C++, performance-oriented problem)
4. Systems and trading-concepts discussion round
5. Culture and ownership round with the senior engineer you will report to

   Compensation: top of market for the experience band, benchmarked against product companies, not services.

Experience and Expertise

• 2+ years of experience in C++ application development.
• Hands-on experience with C++11 or above.
• Strong knowledge of object-oriented programming and software design.
• Deep understanding of STL, multi-threading, socket programming, and data structures.
• Solid grasp of Linux development and debugging techniques.
• Proficient in using GCC, GDB, and Makefile.
• Familiarity with Valgrind and similar analysis tools.
• Experience with version control tools like Git.
• Experience writing and maintaining automated tests.
• Experience in capital markets/trading domain is a plus.

Position Overview:

We are building a new retail merchandising platform on top of that data. It answers what to stock, how much,

and in which store—combining sales, catalogue, and customer data with GeoIQ’s location attributes at retail

scale: thousands of stores, tens of thousands of SKUs, decisions made weekly.

As part of the data science team on this platform, you will build the forecasting, assortment and

recommendation models behind those decisions. The platform is new, so the models are being built now, not

maintained. Hands-on modelling with senior review — what you ship changes what a store actually orders.

Responsibilities:

● Build SKU- and store-level demand forecasts, including for new products with little or no sales history.

● Build assortment models — which products earn shelf space in which stores or store clusters, and how

a range change is likely to play out.

● Build recommendation and ranking models for product discovery, cross-sell and bundling, including

cold-start for new products and customers.

● Engineer features from sales, seasonality, promotions, pricing, catalogue hierarchy, store attributes and

GeoIQ’s location and demographic layers.

● Handle the realities of retail data: intermittent demand, stockouts censoring true demand, returns and

cannibalisation between similar SKUs.

● Design backtests and A/B tests that reflect how planners use the output; monitor deployed models for

drift.

● Build data pipelines and work with engineering to take models to production.

● Communicate findings to merchandising, category, and product teams.

Expectations:

● Proficient in Python

● Work experience: 1–3 years

● Prior experience with building ML models end-to-end

● Prior experience with SQL

● Knowledge of statistical techniques and model evaluation

● Experience with time-series forecasting or recommender systems will be an added advantage

● Experience with retail, e-commerce, supply chain, or spatial data will be an added advantage

Join us early and help build the merchandising brain for retail at national scale — close to the product and the

customer, with the freedom of a platform still being shaped.

As an Engineering Manager, you'll lead efforts to strengthen and optimize our state-of-the-art systems, ensuring high performance, scalability, and efficiency across our suite of trading solutions.

The core responsibilities for the job include the following:

Technical Expertise:

• C++ coding and debugging to strengthen and optimize systems.
• Design and architecture (HLD/LLD) to ensure scalable and robust solutions.
• Implementing and enhancing DevOps, Agile, and CI/CD pipelines to improve development workflows.
• Managing escalations and ensuring high-quality customer outcomes.

Architecture and Design:

• Define and refine the architectural vision and technical roadmap for enterprise software solutions.
• Design scalable, maintainable, and secure systems in line with business goals.
• Collaborate with stakeholders to translate requirements into technical solutions.
• Driving engineering initiatives to foster innovation, efficiency, and excellence.

Project Management:

• Oversee project timelines, deliverables, and quality assurance processes.
• Coordinate cross-functional teams to ensure seamless integration of systems.
• Identify risks and proactively implement mitigation strategies.

Technical Leadership:

• Lead and mentor a team of engineers, fostering a collaborative and high-performance culture.
• Provide technical direction and guidance on complex software engineering challenges.
• Drive code quality, best practices, and standards across the engineering team.

Requirements:

• 10-15 years in the tech industry, with 2-4 years in technical leadership or managerial roles.
• Technical Expertise: Expertise in C++ development, enterprise architecture, and scalable system design, and proficiency in performance optimization, scalability, software architecture, and networking principles.
• Extensive experience managing the full development lifecycle of large-scale software products, from concept to deployment.
• Strong knowledge of STL containers, multi-threading concepts, and algorithms.
• Solid understanding of memory management and efficient resource utilization.
• Microservices Architecture Expertise: Experience in designing and implementing scalable, reliable microservices.
• Strong Communication and Decision-making skills: Ability to clearly articulate trade-offs, make informed decisions, and ensure alignment across stakeholders.
• Commitment to Creating and fostering Engineering Excellence: Deep understanding of best practices, including code quality, testability, security, and release management, and passion for fostering a strong engineering culture and continuously improving developer workflows and tools.
• Self-Driven and Motivated: Ability to operate independently while driving impactful results.

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

As an Engineering Manager, you'll lead efforts to strengthen and optimize our state-of-the-art systems, ensuring high performance, scalability, and efficiency across our suite of trading solutions.

The core responsibilities for the job include the following:

Technical Expertise:

• C++ coding and debugging to strengthen and optimize systems.
• Design and architecture (HLD/LLD) to ensure scalable and robust solutions.
• Implementing and enhancing DevOps, Agile, and CI/CD pipelines to improve development workflows.
• Managing escalations and ensuring high-quality customer outcomes.

Architecture and Design:

• Define and refine the architectural vision and technical roadmap for enterprise software solutions.
• Design scalable, maintainable, and secure systems in line with business goals.
• Collaborate with stakeholders to translate requirements into technical solutions.
• Driving engineering initiatives to foster innovation, efficiency, and excellence.

Project Management:

• Oversee project timelines, deliverables, and quality assurance processes.
• Coordinate cross-functional teams to ensure seamless integration of systems.
• Identify risks and proactively implement mitigation strategies.

Technical Leadership:

• Lead and mentor a team of engineers, fostering a collaborative and high-performance culture.
• Provide technical direction and guidance on complex software engineering challenges.
• Drive code quality, best practices, and standards across the engineering team.

Requirements:

• 6-13 years in the tech industry, with 2-4 years in technical leadership or managerial roles.
• Technical Expertise: Expertise in C++ development, enterprise architecture, and scalable system design, and proficiency in performance optimization, scalability, software architecture, and networking principles.
• Extensive experience managing the full development lifecycle of large-scale software products, from concept to deployment.
• Strong knowledge of STL containers, multi-threading concepts, and algorithms.
• Solid understanding of memory management and efficient resource utilization.
• Microservices Architecture Expertise: Experience in designing and implementing scalable, reliable microservices.
• Strong Communication and Decision-making skills: Ability to clearly articulate trade-offs, make informed decisions, and ensure alignment across stakeholders.
• Commitment to Creating and fostering Engineering Excellence: Deep understanding of best practices, including code quality, testability, security, and release management, and passion for fostering a strong engineering culture and continuously improving developer workflows and tools.
• Self-Driven and Motivated: Ability to operate independently while driving impactful results.

We are looking for a rigorous, systems-minded quantitative leader to own our core commercial intelligence engine end-to-end. This is not a traditional dashboard-building or BI reporting seat. You will define, build, and encode the mathematical logic, unit-economic models, and predictive forecasting systems that power our product and drive real commercial decisions for our brands.

You will sit at the intersection of finance, data science, and product engineering, turning complex commercial data, messy founder inputs, and predictive models into defensible, automated product logic and AI agent workflows.

Key Accountabilities

1. Proprietary Systems: Growth Forecasts & P&Ls

• Growth Forecasting: Build and own the methodology that takes a brand’s current performance and projects a defensible 12-month forecast, complete with a rigorous assumption set.
• Financial Architecture: Construct brand-level P&Ls directly from the forecast, mapping revenue, COGS, contribution margin, and operating costs.
• Variance & Calibration: Continuously run models against real outcomes, measure errors, track actuals against projections, and explain every variance.

2. Productization & Agentic Workflow Design

• Product Logic: Translate sophisticated financial and statistical models into production-ready product logic so the system calculates and delivers outputs natively.
• AI Agent Specification: Define what data agents compute, what internal data/benchmarks they draw on, and what assertions they are strictly barred from making.
• Human-in-the-Loop Orchestration: Replace manual validation with automated product rules while establishing clear review gates for human sign-off before deliverables ship.
• Data Schemas & Engineering Collaboration: Specify data structures, validation rules, and pipelines across structured commercial inputs, partnering directly with engineering to bring models to life in production.

3. Decision Framing & Commercial Rigor

• Decision-First Modeling: Design every analysis backwards from the specific commercial decision it is meant to change.
• Uncertainty & Stated Confidence: Size uncertainty statistically, state clearly what sparse or self-reported data cannot support, and attach a stated confidence level to every output.
• Benchmark Library Management: Define and version the benchmark repository (category, stage, market) to evaluate brands accurately even when data is sparse.

4. Enablement & Commercial Communication

• Cross-Functional Communication: Present complex numbers, models, and uncertainties to founders and operators with clarity and authority.
• Quantitative Literacy: Raise the analytical bar across the business by teaching teams how to read and interpret model outputs correctly.
• Documentation & Standards: Write airtight documentation and standards for data/finance deliverables so cross-functional delivery teams can execute autonomously.

What This Seat Is Not

• Not a BI/Reporting Seat: Dashboards are merely one downstream output of this work; your primary focus is proprietary modeling and product architecture.
• Not Research Without Action: We judge models by the decisions they actually changed, not academic complexity.
• Not a Data Engineering Seat: You will specify pipelines, schemas, and logic for engineering to build—you won't be maintaining core infrastructure tables yourself.

Ideal Background & Qualifications

Must-Haves

• Experience: 6–8 years in growth, marketing, commercial, or product analytics within DTC, e-commerce, marketplaces, or specialized platforms.
• Proven Track Record: End-to-end ownership of a forecasting, unit-economics, or media-mix model that commercial teams relied on for high-stakes budget or strategic decisions. You can name the decisions your model changed and what followed.
• DTC Unit Economics Mastery: Deep fluency in CAC, AOV, COGS, contribution margin, LTV, payback period, breakeven, retention cohorts, and working capital reconciliation.
• Modelling with Incomplete Data: Demonstrated ability to build robust outputs from banded, self-reported, or partial founder inputs while explicitly quantifying confidence levels.
• Statistical Rigor: Strong foundation in sizing uncertainty, managing sample limitations, and maintaining analytical integrity.
• Technical Depth: Hands-on daily fluency in SQL and Python.
• Product & Engineering Collaboration: Experience working directly with engineering teams to turn quantitative models or metric definitions into production software.
• Communication: Exceptional presence and ability to defend underlying assumptions and model uncertainty under direct challenge.

Nice-to-Haves

• Media mix modeling (MMM), incrementality testing, or causal measurement methodologies.
• Applied machine learning experience (forecasting, propensity, or segmentation models in production).
• Experience evaluating generative AI or multi-agent system outputs.
• Familiarity with modern data stack layers (Snowflake, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).
• Exposure to multi-brand ecosystems via agency, consulting, or platform environments.
• Experience in early-stage product environments where models and products were co-developed from scratch.

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

Key Responsibilities

 Strong understanding of Machine Learning algorithms (supervised and unsupervised)

 Hands-on experience with Deep Learning frameworks (TensorFlow, PyTorch, or similar)

 Experience in NLP techniques and libraries (NLTK, spaCy, Hugging Face, etc.)

 Solid knowledge of Statistics, probability, and data analysis methods

 Proficiency in SQL for querying relational databases

 Strong programming skills in Python (or similar languages)

 Excellent communication skills with the ability to explain complex concepts simply

Required Qualifications

 3+ years of hands-on experience as a Data Scientist or in a similar role.

 Strong expertise in classical machine learning and regression modeling.

 Solid understanding of statistics, including probability, distributions, hypothesis testing,

and correlation analysis.

 Proficiency in Python with libraries such as: scikit-learn pandas NumPy

 Experience working with structured/tabular data.

 Strong problem-solving and analytical thinking skills.

 Ability to clearly explain models and results to non-technical stakeholders.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

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