Quantitative Developer - Internship (WFH)

About the internship:We are seeking a highly motivated 'Quantitative Developer Intern' to contribute to the development and optimization of data-driven algorithms and quantitative models. This is an exceptional opportunity to work closely with senior developers and researchers, gaining practical, hands-on experience in building, testing, and scaling cutting-edge financial systems.

Selected intern's day-to-day responsibilites include:

1. Assist in the development, implementation, and testing of quantitative models and trading algorithms.

2. Analyze large, high-frequency datasets and develop tools to streamline data ingestion and analysis workflows.

3. Support the rigorous backtesting and validation of models using historical and real-time market data.

4. Collaborate with the technology team to optimize code for performance and scalability, primarily focusing on low-latency systems.

5. Create and maintain clear, comprehensive documentation for all algorithms, analysis workflows, and developed tools. Who can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 15th Sep'26 and 20th Oct'26are available for duration of 6 monthshave relevant skills and interestsare Computer Science Engineering studentsStipend:INR₹ 14,000 - 19,000 /monthDeadline:[hidden]:59:59Other perks:CertificateSkills required:Java, Python and ReactOther Requirements:A. Required:1. Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Mathematics, Statistics, Engineering, or a closely related quantitative field.2. Strong proficiency in at least one of the following core languages:3. Python: Extensive experience with the scientific stack (Pandas, NumPy, SciPy) is required.4. Experience with relational or non-relational databases (e.g., SQL/NoSQL) for handling and querying large datasets.5. Familiarity with data analysis and introductory machine learning techniques.6. Basic understanding of financial markets, time-series data, and fundamental quantitative methods (e.g., statistical concepts, risk management).B. Preferred Skills:1. Programming Ecosystem: Exposure to additional languages like Java or C# for software integration.2. Machine Learning: Experience with advanced machine learning libraries like Scikit-learn, TensorFlow, or PyTorch for building predictive models.3. Quantitative Frameworks: Experience with specialized backtesting frameworks (e.g., QuantConnect, Backtrader, Zipline).4. Financial Knowledge: Understanding of algorithmic trading strategies (e.g., market making, statistical arbitrage, HFT).5. Tools & Platforms: Experience with data visualization (e.g., Plotly, Tableau) and version control systems (e.g., Git).6. Familiarity with financial data platforms (e.g., Bloomberg or Refinitiv/Reuters).About Company:Finflock Systems is a leading fintech startup combining the power of advanced technologies such as cloud computing, machine learning, and AI with mathematics, statistics, and operations research. The company generates state-of-the-art decision-enabling analytics and superior research intelligence via its various products, platforms, and services. It offers scalable intelligent solutions for businesses in the finance industry of all sizes.Additional information:

Stipend Structure:

Fixed pay: ₹ 12,000 - 15,000 /month

Incentive pay: ₹ 2,000 - 4,000 /month

Responsibilities

We are seeking a highly motivated 'Quantitative Developer Intern' to contribute to the development and optimization of data-driven algorithms and quantitative models. This is an exceptional opportunity to work closely with senior developers and researchers, gaining practical, hands-on experience in building, testing, and scaling cutting-edge financial systems.

Selected intern's day-to-day responsibilites include:

1. Assist in the development, implementation, and testing of quantitative models and trading algorithms.

2. Analyze large, high-frequency datasets and develop tools to streamline data ingestion and analysis workflows.

3. Support the rigorous backtesting and validation of models using historical and real-time market data.

4. Collaborate with the technology team to optimize code for performance and scalability, primarily focusing on low-latency systems.

5. Create and maintain clear, comprehensive documentation for all algorithms, analysis workflows, and developed tools.