Senior Data Scientist

About Symphony Technology Group (STG)

STG is a Silicon Valley (California) based private equity firm that has a long and successful track record of transforming high potential software and software‑enabled services companies, as well as insights‑oriented companies into definitive market leaders. The firm brings expertise, flexibility, and resources to build strategic value and unlock the potential of innovative companies. Partnering to build customer‑centric, market winning portfolio companies, STG creates sustainable foundations for growth that bring value to all existing and future stakeholders. The firm is dedicated to transforming and building outstanding technology companies in partnership with world class management teams. Founded in 2002, STG's portfolio has consisted of more than 50 global companies, and the firm currently manages over $12 billion in assets under management (as of March 2025). Learn more: https://stg.com

About STG Labs

STG Labs is the incubation center for many of STG's portfolio companies, building their engineering, professional services and support delivery teams in India. STG Labs offers an entrepreneurial start‑up environment for software and AI engineers, data scientists and analysts, project and product managers and provides a unique opportunity to work directly for a software or technology company. STG has supported its portfolio companies in building teams in India since 2003, and STG Labs is headquartered in a 19,000 square foot office in Bangalore, with teams across multiple locations in India. Learn more: https://stglabs.com

Senior Data Scientist to build a wide variety of systematic machine learning models and deploy them in production. These models must meet high accuracy SLAs, scale to big‑data volumes in production, and be parallelizable to run on distributed computing infrastructure. The ideal candidate has deep technical expertise in NLP, deep learning, pattern mining, and model evaluation and monitoring, and is conversant with using GenAI‑based techniques at each stage of the data‑driven product lifecycle, from development and deployment to maintenance.

Key Responsibilities

Model Design & Development

• Build production‑grade models: efficient machine learning models with high accuracy and broad generalizability, within defined constraints of cost, time, and quality.
• Contribute to model selection: help choose the optimal family of machine learning models and the optimal model topography to solve business problems.
• Conduct trade‑off analysis: assess model performance across the defined constraints of cost, time, and quality.
• Stay hands‑on: carry out data cleansing, data transformation, model building, and evaluation.

Model Lifecycle & Deployment

• Build and maintain the end‑to‑end pipeline: covers continuous model evaluation, anomaly detection, and model retraining.
• Test and package models for release: performance testing and model containerization.

Leadership & Communication

• Mentor and guide the team: support junior data scientists and analysts to deliver results.
• Communicate clearly: explain context, data, solutions, and implications to the team, leadership, and stakeholders.

Required Qualifications & Skills

Must‑Have (Hands‑on)

• 6+ years of hands‑on experience and theoretical knowledge of Natural Language Processing, Deep Learning, and related Machine Learning domains.
• Deep theoretical and practical understanding of different families of machine learning models, data sampling techniques, pattern mining, statistical modelling, and model generalization concepts.
• Solid programming skills in Python, ML frameworks (e.g., scikit‑learn, TensorFlow, NLTK, SageMaker), Shell programming, and SQL.
• Hands‑on experience with GenAI and LLM tools (e.g., LLaMA), including Retrieval‑Augmented Generation (RAG), fine‑tuning, integration with AWS Bedrock, and secure deployment practices such as managing closed model API key access.
• Experience working with huge datasets and big data technologies.
• Strong analytical and quantitative problem‑solving ability, working independently or in a team to solve complex problems.
• Strong interpersonal and communication skills, with the ability to tell a clear, concise, actionable story with data to team members at all levels of the company.

Secondary Skills

• Experience setting up and using Big Data and Machine Learning infrastructure across leading cloud providers, as well as standard ML toolkits.
• Experience in any one of these sectors: Wealth Management, Retail Banking, Insurance, or Credit Underwriting.

Education / Qualifications

• Master's degree in Computer Science, Data Science, Mathematics, or Statistics.

Work Mode

Hybrid — This role adheres to the organization's hybrid work policy, requiring presence at the office on three designated days per week.