Head of Algorithmic trading Neo Wealth and Asset Management

5.0 - 9.0 years

0 Lacs

maharashtra

On-site

Neo Group is a new age, focused Wealth and Asset Management Platform that aims to provide clients with transparent, cost-efficient, and unbiased solutions. Through its various business lines, Neo Group serves a vast diaspora of institutional and retail customers across India, with a view to empowering them to pursue their dreams. Assisting in India's glorious aspiration on self-reliance and egalitarian prosperity is Neo's underlying goal, and we are proud to stand united in that journey. We are building the next billion tech for the best wealth management platform and financial ecosystem. The organization was founded by Nitin Jain (Ex CEO of Nuvama Wealth Management) and backed by Peak XV (earlier Sequioa), MUFG and Euclidean Capital as minority investors. Neo Group is profitable from day 1 with Asset Management boasting an AUM of approximately 12,500 Cr under 3 funds and Neo Wealth Management having Asset Under Advisory of around Rs 48,000 Cr. Neo Markets is a multi-asset, multi-strategy trading business of the Neo Group, striving for superior risk-adjusted returns. The aim and unflinching focus are to be the best analytical and algorithmic trading desk in the country. The desk prides itself on delivering non-correlated superior risk-adjusted returns across market cycles. The organization values individuals who bring a sense of ownership and passion, along with the grit to think, ideate, solve problems, build systems, and take conscious risks to make profits. Position Title: Head of Algo Infrastructure and QA Location: Lower Parel, Mumbai Role Overview: This role encompasses Algo Platform Development, Backtesting Infrastructure, QA/Testing Automation, and DevOps, and is critical to the stability, performance, and evolution of the trading ecosystem. The incumbent will closely collaborate with quantitative researchers, traders, and senior leadership to deliver scalable, high-performance systems for real-time trading. Job Responsibilities: Lead and manage cross-functional teams including Algo platform developers, Backtesting infrastructure engineers, QA/test automation team, and DevOps and CI/CD engineers. Design and oversee the architecture of scalable trading systems. Build and enhance tools for backtesting, simulation, and production deployment. Own the development and execution of QA automation strategies. Ensure operational excellence through robust DevOps practices and monitoring systems. Collaborate with quants, traders, and product teams to understand requirements and translate them into technical solutions. Implement best practices in software development, testing, deployment, and system monitoring. Drive technical decision-making, mentor engineers, and grow technical leadership across the team. Required Skills & Qualifications: 5-7 years of experience in software engineering. Strong programming background in C++. Experience in building real-time systems, preferably in trading or financial services. Proven track record in managing high-performance teams. Deep understanding of DevOps tooling, CI/CD pipelines, and automation frameworks. Exposure to backtesting frameworks and event-driven architectures. Strong understanding of system design, performance tuning, and production reliability. Excellent communication skills. Ability to work with both technical and non-technical stakeholders.,

Posted 3 days ago

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Quantitative Researcher Coinsburg Ventures Pvt Ltd

3.0 - 7.0 years

0 Lacs

coimbatore, tamil nadu

On-site

You are a skilled Quantitative Researcher who will be joining our team to develop advanced algorithmic trading strategies for cryptocurrency markets. Leveraging your deep quantitative skills, market insights, and statistical modeling, you will be responsible for researching, designing, and testing trading strategies that capture alpha in digital asset markets. Your key responsibilities will include researching and developing quantitative trading strategies for crypto markets across spot, perpetual futures, and derivatives. You will analyze market microstructure, order flow, and liquidity to identify trading opportunities. Additionally, you will design and implement statistical and machine learning models for signal generation, risk forecasting, and anomaly detection. It will be essential for you to perform extensive backtesting, simulation, and performance analysis using historical and live data. Furthermore, optimizing strategies for execution costs, slippage, latency, and market impact will be part of your responsibilities. Collaboration with developers to translate research into production-grade algorithmic trading systems is also a key aspect of the role. Monitoring live strategies and conducting post-trade analysis to refine and improve models, staying updated on market trends, blockchain analytics, protocol updates, and evolving market structure, and contributing to research documentation, strategy reports, and knowledge sharing within the team are also part of your duties. The required skills and qualifications for this role include a strong background in quantitative research, statistical modeling, or applied mathematics, solid programming skills in Python, R, or similar languages for data analysis and prototyping, experience with time series analysis, signal processing, and statistical inference, ability to work with large datasets, proficiency in data cleaning, feature engineering, and data visualization, understanding of trading concepts such as PnL attribution, Sharpe ratio, drawdown, and risk management, interest in cryptocurrency markets and familiarity with crypto-specific market dynamics, excellent problem-solving and critical-thinking skills, and ability to communicate complex quantitative ideas clearly to technical and non-technical stakeholders. Preferred qualifications for this role include prior experience in algorithmic trading or systematic strategy development (crypto or traditional markets), knowledge of crypto market microstructure, DeFi protocols, and blockchain data analytics, familiarity with exchange APIs, order types, and execution logic, experience with backtesting frameworks and simulation environments, machine learning experience applied to financial markets (e.g. feature selection, ensemble models, reinforcement learning), and an academic background in quantitative disciplines (e.g. Mathematics, Statistics, Physics, Computer Science, Engineering, Finance).,

Posted 5 days ago

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