Data Science Lead / Head of Data Science (2+ yrs)

Spectrum; Mumbai; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Spectrum, a leading financial services firm, is seeking a Senior Data Science Lead in Mumbai to define the 3-year vision for data science, ML, and AI within the brokerage ecosystem. You will drive model development, NLP solutions, and generative AI for market research and client onboarding.

You will collaborate with Product, Risk, Tech, and Trading Operations to deploy scalable data products, oversee data lakehouse and streaming pipelines, and mentor a team of data scientists and ML engineers.

Qualifications

• 7+ years of core data science, ML, and quantitative modeling experience, preferably in Stock Broking, Fintech, Quantitative Finance, or Banking.
• Strategic vision: translate business problems into data strategies and drive revenue or efficiency gains through quantitative models.
• NLP & Generative AI: deploy NLP models for sentiment analysis, chatbots, and unstructured financial text extraction.
• Core ML & Data Analytics: strong command of Python, R, SQL, ML frameworks (PyTorch, TensorFlow, Scikit-Learn), and advanced statistics.
• Data Engineering & Cloud Infra: experience with Spark, Kafka, Snowflake/BigQuery and cloud platforms (AWS/Azure/GCP).
• Education: Masters or Ph.D. in relevant field.

Responsibilities

• Define the 3-year vision, roadmap, and architecture for DS, ML, and AI within the brokerage ecosystem.
• Lead development of advanced ML models, NLP solutions, and Generative AI for market research and onboarding.
• Collaborate with Product, Risk, Tech, and Trading Ops to deploy scalable data products that improve retention, volume, risk, and personalization.
• Oversee data lakehouse architectures, ETL pipelines, and real-time streaming for market feeds and logs.
• Hire, mentor, and manage a team of Data Scientists, ML Engineers, and Data Analysts; establish MLOps and governance standards.
• Ensure all models comply with financial regulations and data privacy requirements.

Skills

Strategic planning

NLP & Generative AI

Core ML & Data Analytics

Data Engineering & Cloud Infra

Leadership & Mentorship

Education

Masters or Ph.D. in CS/DS/Statistics/Math/Quant Finance

Tools

Python

R

SQL

PyTorch

TensorFlow

Spark

Kafka

Snowflake/BigQuery

Job description

Key Responsibilities

• Strategic Planning: Define the 3-year vision, roadmap, and architecture for Data Science, Machine Learning, and AI within the brokerage ecosystem.
• AI & Machine Learning Leadership: Lead the development of advanced ML models, Natural Language Processing (NLP) solutions, and Generative AI applications for market research, sentiment analysis, client onboarding, and automated insights.
• Product Collaboration: Work closely with Product, Risk, Tech, and Trading Operations teams to deploy scalable data products that improve user retention, trade volume, risk management, and portfolio personalization.
• Data Engineering & Infrastructure: Oversee data lakehouse architectures, ETL pipelines, and real-time streaming architectures to handle high-frequency market feeds, tick data, and transactional logs.
• Team Leadership & Mentorship: Hire, mentor, and manage a team of Data Scientists, ML Engineers, and Data Analysts. Establish standards for code quality, MLOps, model monitoring, and governance.
• Governance & Regulatory Compliance: Ensure all quantitative models and AI implementations comply with financial regulations, data privacy laws, and model risk management standards.

Required Qualifications & Skills

• Experience: 7+ years of core data science, machine learning, and quantitative modeling experience, preferably in Stock Broking, Fintech, Quantitative Finance, or Banking.
• Strategic Vision: Proven track record of translating business problems into data strategies and driving revenue or efficiency gains through quantitative models.
• NLP & Generative AI: Practical experience deploying NLP models for sentiment analysis on financial news/filings, chatbots, and unstructured financial text extraction.
• Core ML & Data Analytics: Strong command of Python, R, SQL, Machine Learning frameworks (PyTorch, TensorFlow, Scikit-Learn), and advanced statistical analytics.
• Data Engineering & Cloud Infrastructure: Deep understanding of big data systems (Spark, Kafka, Snowflake/BigQuery) and MLOps platforms on AWS, Azure, or GCP.
• Education: Masters or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, Quantitative Finance, or a related field.