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Careers

Lead Data Scientist

Lead the data science workstream across one or more client engagements — owning technical direction, a small team of data scientists, and the calls a workstream's success depends on.

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Closed — not currently hiring

We're not actively recruiting for this position right now. Keep this page in mind, or share your profile below so our team can reach out if it reopens.

Practice

Data Science

Location

Remote

Employment type

Full Time

Experience

7–10 Years

Role Overview

You will lead the data science workstream on one or more client engagements, owning the technical approach end to end and the team delivering it. This is a hands-on lead role, not a hands-off one: you will still design models and review code, but you will also set the technical direction, defend it to client stakeholders, and make the calls that a workstream's success depends on. You will manage a small team of data scientists, split work across a project, and be the point of technical accountability when something needs to go right. If you have led a technical workstream through ambiguity to a result a client actually used, and want to do that with more scope and less oversight, this role is built for you.

What You Will Do

  • Own the technical approach for a data science workstream or project, from problem framing through to production handoff
  • Lead and mentor a small team of data scientists, reviewing their work and setting the technical bar
  • Break down ambiguous client problems into a workstream plan with clear milestones and owners
  • Make build-vs-buy and modelling-approach calls, and defend them to technical and non-technical stakeholders
  • Act as the primary technical point of contact for client stakeholders on your workstream
  • Review model architecture, experiment design and code across the team before it reaches a client
  • Escalate and resolve delivery risks early, including scope, data quality and timeline risks
  • Contribute to proposals and solutioning for new or expanding client engagements
  • Partner with data engineering and AI engineering leads on cross-functional workstreams

Technical Skills

  • Languages & Core Libraries: Expert-level Python (pandas, numpy, scikit-learn) and advanced SQL. Comfortable reading and reviewing others' code, not only writing your own.
  • Machine Learning: Deep expertise in at least two areas — ensembling, time series and forecasting, NLP, computer vision, or causal inference. Strong grounding in applied statistics and experimental design.
  • Solution Architecture: Ability to design an end-to-end modelling approach for an ambiguous business problem, including data requirements, evaluation strategy and production path.
  • Experimentation & Evaluation: Experiment design, A/B testing, offline and online evaluation, and the judgement to catch a misleading metric before it reaches a client.
  • MLOps & Production: Model versioning, registries, reproducibility, and experiment tracking with MLflow or an equivalent. Comfortable owning a model through to production and monitoring it after launch.
  • Cloud: Hands-on depth with at least one of AWS, GCP or Azure, including their managed ML services, at a level where you can advise on architecture choices.
  • Leadership & Delivery: Experience running a workstream — planning, task allocation, code and design review, and reporting delivery status to both technical and client audiences.

Experience & Qualifications

  • 7 to 10 years of applied data science experience, including at least 2 years leading a workstream, project or small team
  • A track record of taking multiple models into production or real decision use, across more than one domain or client
  • Experience managing or mentoring other data scientists, with visible growth in the people you've worked with
  • Bachelor's or Master's degree in Statistics, Computer Science, Mathematics or a related quantitative field

Good to Have

Not required, but a strong plus if your experience already includes any of the following.

  • Experience in a consulting or professional services environment, managing client relationships directly
  • Exposure to retail, consumer goods, manufacturing or supply chain domains
  • Experience contributing to technical proposals, scoping or pre-sales conversations
  • Formal people-management experience, including performance reviews or career development conversations

Why Xunova

Remote-first, built on trust

A genuinely remote-first team, built around trust and ownership rather than hours logged.

Direct access to leadership

Direct exposure to client leadership from day one, with no layers between you and the work that matters.

Ownership that shapes the work

A high-growth environment where your judgment shapes how we work, not just what we deliver.

Colleagues who hold a high bar

A small, deliberately high-caliber team that takes the craft seriously and holds a high bar for each other.

How we hire

A straightforward, three-conversation process, no take-home busywork disconnected from the job.

Remote · Full Time
  1. 01

    Introductory conversation

    A conversation about your experience, what you're looking for, and what we're building together.

  2. 02

    Technical discussion

    Grounded in real engineering problems, not abstract puzzles disconnected from the work.

  3. 03

    Team conversation

    Time with the team you'd actually be working with, in both directions.

Not currently hiring for Lead Data Scientist

We'll keep your profile on file and reach out directly if this position — or a similar one — reopens.

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