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

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.