About this role
We are looking for a Lead Data Scientist comfortable framing an ambiguous business problem, someone with broad methodological range and the flexibility to choose the right approach for each problem.
The problems we work on cut across supervised and unsupervised learning, causal inference, optimization, recommendation, forecasting, and increasingly LLM-based systems including Agentic AI. Candidate should be able to use the right tool for the problem while keeping the approach driven by the business question & hence lead by example. This is a hands-on (~70% IC work involving modeling, coding, system design and ~30% mentorship and stakeholder engagement.
What you’ll do
Partner with stakeholders to frame the problem & develop methodology across the full spectrum. Work directly with business leaders and domain experts to translate ambiguous questions into well-posed analytical problems, align on success metrics, communicate trade-offs clearly, and recommend the most effective approach: Supervised Learning, Unsupervised Learning, NLP, Generative / Agentic AI, Causal Machine Learning, Optimization, Recommendation Systems and Forecasting or rule-based decision based on impact, constraints, and maintainability
Apply LLMs and agentic systems pragmatically, and drive adoption of evolving capabilities. Continuously evaluate new models, tooling and patterns; run fast, measurable prototypes; and scale the winners into production RAG/agent workflows i.e., owning evaluation, guardrails and hallucination control
Build and own end-to-end production systems. delivering robust, observable services that run reliably at scale i.e., from data cleaning and feature engineering through model development, evaluation, deployment, monitoring, retraining and incident
Set the technical bar and lead by example. Define and uphold standards for experimental design, offline/online evaluation, A/B testing, causal validity, model governance and reproducibility then reinforce them through hands-on mentorship, pairing on hard problems and thoughtful code/design reviews that grow the team’s craft and depth
What you’ll need
This role is designed for a hands-on Data Science leader who combines strong fundamentals with strong engineering instincts. You’ll likely recognize yourself in many of the following:
Strong judgment. You balance rigor, speed and maintainability and can explain trade-offs in a way that helps teams and stakeholders make good decisions
Hands-on, by choice. You write production-quality Python code, review code thoughtfully and treat coding as a core part of how you build and think
Systems builder. You’ve taken solutions from a blank repo to production systems that real users or business processes depend on
Strong in fundamentals. You can explain why methods work, when they break and what assumptions they rely on in clear, simple language. Linear algebra, probability, optimization and statistical inference are tools you use actively
Comfortable with ambiguity. You can take a vague problem, clarify goals and constraints, and turn it into a well-defined plan before selecting the approach
Experience
8+ years in Applied ML / Applied AI (including LLM & GenAI), with 6+ years hands-on experience building and deploying models in production environments
Bachelor's or Master's (or PhD) in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field. Equivalent demonstrable expertise is welcome. Strong working knowledge of classical ML and statistics: regularized regression, tree-based methods, gradient boosting, clustering, dimensionality reduction, hypothesis testing, and experimental design
Modern LLM stack: RAG, agentic workflows, evaluation frameworks, prompt engineering, fine-tuning trade-offs, and vector databases
Deep learning fundamentals: understanding when to apply deep learning approaches and how to train and evaluate them effectively in practice
NLP fundamentals: text preprocessing, embeddings, similarity/search, topic modeling, classification, and evaluation
Causal ML: experience with at least one: DML, uplift modeling, IV, propensity scoring, synthetic control, or difference-in-differences, applied in a decision-making or production context
Optimization: linear/integer programming, constrained optimization, bandits, or RL applied to real-world problems
Expert-level Python. Comfortable with the scientific stack (NumPy, pandas, scikit-learn, PyTorch) and with writing clean, tested, modular code
Working knowledge of Agentic AI Frameworks like Langchain, Langgraph and Deepagents or equivalent
Cloud Computing (AWS / Azure / GCP) - model training, deployment, scaling, cost-awareness
What we offer
Problems worth solving. Real ambiguity, real scale, real impact
A seat at the table. Direct partnership with leadership on what we build and why
Freedom in tooling and method. Pick the right approach. We trust your judgment
Competitive salary, generous paid time off policy, charity match program, Group Medical Insurance, Parental Leave, Employee Assistance Program (EAP) and more!
Collaborative, team-oriented culture that embraces diversity
Professional development and unlimited growth opportunities
#LI-PM3
Who are we?
At Gartner, Inc. (NYSE:IT), we guide the leaders who shape the world.
Our mission relies on expert analysis and bold ideas to deliver actionable, objective business and technology insights, helping enterprise leaders and their teams succeed with their mission-critical priorities.
Since our founding in 1979, we’ve grown to 20,000 associates globally who support over 13,000 client enterprises in ~90 countries and territories. We do important, interesting and substantive work that matters. That’s why we hire associates with the intellectual curiosity, energy and drive to want to make a difference. The bar is unapologetically high. So is the impact you can have here.
What makes Gartner a great place to work?
Our vast, virtually untapped market potential offers limitless opportunities – opportunities that may not even exist right now – for you to grow professionally and flourish personally. How far you go is driven by your passion and performance.
We hire remarkable people who collaborate and win as a team. Together, our singular, unifying goal is to deliver results for our clients.
Our teams are inclusive and composed of individuals from different geographies, cultures, religions, ethnicities, races, genders, sexual orientations, abilities and generations.
We invest in great leaders who bring out the best in you and the company, enabling us to multiply our impact and results. This is why, year after year, we are recognized worldwide as a great place to work.
Gartner is the world authority on AI
At Gartner, you’ll join a company at the very center of the AI revolution. Gartner has proactive, objective guidance throughout clients’ AI journeys. We set the standard for how organizations leverage artificial intelligence to drive meaningful impact. You’ll have access to unmatched resources, expertise, and technology, and play a key role in helping Gartner and our clients innovate and grow as we leverage AI to transform business and technology landscapes.
It’s an exciting time to be at Gartner, with limitless opportunities to make a real impact, grow your skills, and build a lasting, meaningful career in a field that’s reshaping the way we operate. If you’re passionate about AI and want to be part of a team that’s guiding the leaders who shape the world, Gartner is the place for you.
What do we offer?
Gartner offers world-class benefits, highly competitive compensation and disproportionate rewards for top performers.
In our hybrid work environment, we provide the flexibility and support for you to thrive — working virtually when it's productive to do so and getting together with colleagues in a vibrant community that is purposeful, engaging and inspiring.
Ready to grow your career with Gartner? Join us.
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