Head Data Science

Good co India; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Good co India is seeking a senior leader to define and drive the organization's data science and AI strategy in line with business goals. You will mentor a team of data scientists, ML engineers, and analytics professionals to build, validate, deploy, and monitor production ML/AI models.

You will partner with product, engineering, and executive leadership to translate business problems into scalable analytical solutions, manage roadmaps and budgets, and measure impact through KPIs and ROI.

Qualifications

• 8–10 years of experience in Data Science, ML, or AI with leadership.
• Bachelor's or master's degree in CS, Data Science, AI, Stats, or related field.
• Strong Python, SQL, statistics, ML algorithms, deep learning, and predictive analytics.
• Hands-on with Generative AI, LLMs, NLP, and RAG architectures.
• Experience deploying production ML models with MLOps on AWS, Azure, or GCP.
• Proven ability to lead cross-functional teams and manage multiple projects.

Responsibilities

• Define and execute data science and AI strategy aligned with business objectives.
• Lead and mentor data scientists, ML engineers, and analytics professionals.
• Oversee design, validation, deployment, and monitoring of ML/AI models.
• Identify opportunities via advanced analytics and predictive modeling.
• Drive Generative AI, LLMs, NLP, and automation initiatives.
• Establish best practices for governance, experimentation, MLOps, and responsible AI.
• Collaborate with product, engineering, and leadership to translate problems into scalable solutions.
• Manage data science project roadmaps, budgets, and delivery timelines.
• Track model performance, KPIs, ROI, and measurable impact.
• Evaluate emerging technologies and build scalable AI/ML solutions.

Skills

Python

SQL

Statistics

ML algorithms

Deep learning

Predictive analytics

Generative AI

LLMs

NLP

MLOps

Cloud AWS

Cloud Azure

Cloud GCP

Stakeholder mgmt

Communication

Strategic thinking

Problem solving

Data governance

Education

Bachelor's/Master's in CS/Data Science/AI/Statistics

Tools

PyTorch

TensorFlow

Airflow

MLflow

Docker

Job description

Role & responsibilities

• Define and execute the organization's data science and AI strategy aligned with business objectives.
• Lead, mentor, and develop data scientists, machine learning engineers, and analytics professionals.
• Oversee the design, development, validation, deployment, and monitoring of machine learning and AI models.
• Identify business opportunities through advanced analytics, predictive modelling, and data-driven decision-making.
• Drive Generative AI, Large Language Models (LLMs), NLP, and automation initiatives where relevant.
• Establish best practices for data governance, model validation, experimentation, MLOps, and responsible AI.
• Collaborate with product, engineering, business, and executive leadership to translate business problems into scalable analytical solutions.
• Manage data science project roadmaps, budgets, resources, and delivery timelines.
• Track model performance, business KPIs, ROI, and the measurable impact of data science initiatives.
• Evaluate emerging technologies and build scalable, reliable, and cost-effective AI/ML solutions.

Preferred candidate profile

• 8 to 10 years of experience in Data Science, Machine Learning, Advanced Analytics, or AI, with demonstrated leadership experience.
• Bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related quantitative field.
• Strong proficiency in Python, SQL, statistics, machine learning algorithms, deep learning, and predictive analytics.
• Hands‑on experience with Generative AI, LLMs, NLP, RAG architectures, and AI application development.
• Experience deploying and managing production‑grade ML models using MLOps practices and cloud platforms such as AWS, Azure, or GCP.
• Proven ability to lead cross‑functional teams, mentor technical talent, and manage multiple strategic projects.
• Strong understanding of data engineering, data architecture, data pipelines, and large‑scale data processing.
• Demonstrated success translating complex business challenges into actionable insights and measurable business outcomes.
• Excellent stakeholder management, communication, strategic thinking, and problem‑solving skills.
• Experience with enterprise‑scale AI adoption, model governance, data privacy, and responsible AI practices is preferred.