Data Scientist

2 days ago


Mumbai, Maharashtra, India Qode Full time

**Company Overview**

Qode is a pioneer in quant investing in India, with a research-driven, technology-powered approach since 2016. We combine quant-driven models with active portfolio management to navigate dynamic markets effectively.

 

Position Overview

The Data Scientist will support Qode's data ecosystem, ensuring accuracy, accessibility, and efficiency in data-driven decision-making for trading and investment strategies. This role involves working with complex financial datasets, optimizing data pipelines, and assisting in research operations across multiple asset classes.

 

Data Collection & Management

Gather, clean, and maintain structured and unstructured financial data from sources like Bloomberg, NSE, BSE, and broker platforms.

 

Data Processing & Analysis

Preprocess large datasets, transform raw data into meaningful insights, and support quantitative research and trading operations.

 

Trading & Market Analytics

Track key trading metrics such as volatility, options data (Greeks, IV, open interest), and market trends.

 

Database & Tools Management

Work with SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and cloud-based storage to manage real-time and historical data.

 

Reporting & Visualization

Develop dashboards and reports to support traders, quants, and portfolio managers in data-driven decision-making.

 

Compliance & Data Security

Ensure data integrity, maintain logs, and comply with regulatory guidelines.

 

Collaboration

Work closely with quant researchers and traders to support data needs for backtesting, strategy development, and portfolio management.

 

This role is ideal for someone passionate about data, markets, and analytics, with a keen eye for accuracy and optimization.

 

Qualifications & Skills

Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, or a related quantitative field.

 

Technical Expertise

Database Proficiency: Expert knowledge of PostgreSQL, MySQL, and MongoDB for data storage and management.

Familiarity with Redis for real-time data processing.

Programming: Advanced Python skills (pandas, NumPy, scikit-learn).

Data Engineering: Experience with ETL processes and data pipeline automation using tools like Apache Airflow or Prefect.

Expertise in handling high-frequency trading data and managing time-series datasets.

Visualization: Proficiency in Tableau, Power BI, or custom Python-based visualization libraries (e.g., Plotly, Matplotlib).

Big Data Frameworks: Knowledge of Hadoop, Spark, or Kafka for processing large financial datasets.

APIs and Integration: Experience working with financial data APIs (e.g., Bloomberg, Alpha Vantage, Quandl).

 

Other Skills

Strong analytical and problem-solving skills with a focus on financial data metrics.


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