Engage a senior
A senior fractional data science manager who runs the data science operation a few days a week: model strategy, shipped models, and the standard for how the business builds and trusts them, without a full-time hire.
A senior fractional data science manager who runs the data science operation a few days a week: model strategy, shipped models, and the standard for how the business builds and trusts them, without a full-time hire
Most teams do not have a full-time role's worth of data science leadership, but they need someone senior to own how models get built and trusted. A fractional data science manager gives you that a few days a week, for a fraction of a full-time cost.
A fractional data science manager runs the data science operation day to day: the models in production, the pipelines, the delivery cadence, and the data scientists who build them. Models ship into production and keep earning, rather than dying in a notebook, owned by an operator who has done it at scale.
Our operators have unlocked tens and hundreds of millions with customer data platforms, recommendation engines, and predictive models. The job is the decision the model improves, owned on an ongoing basis.
A fractional Data Science Manager runs the data science operation day to day: the models in production, the pipelines, the delivery cadence, and the data scientists who build them. It is leadership on an ongoing, part-time basis, sized for teams that need senior data science direction but not a full-time hire. The model puts a senior operator who has built and shipped models at scale on your data science a few days a week.
Good data science leadership starts from the decision a model will improve and what it is worth, then makes sure the model ships into the workflow that uses it. The manager runs the delivery that makes it happen.
Models depend on clean, reliable data and need maintenance as behaviour shifts. Our operators keep the pipeline and the models healthy as an ongoing function.
A fractional leader leaves the team able to keep building and shipping models on its own: documented pipelines, a roadmap, and the discipline of tying models to decisions.
Most engagements own some combination of these on an ongoing, part-time basis.
| Feature | Chameleon fractional data science manager | Data science agency / contractor | Full-time data science manager hire |
|---|---|---|---|
| What you get | A senior leader owning DS ongoing | A model; leadership not included | A permanent employee, if the search lands |
| Seniority | 14-20+ years, models shipped at scale | Senior on pitch; varies on delivery | Whoever you can hire and afford |
| Ships to production | Yes, owns pipeline + deployment | Often model-only; plumbing is yours | Eventually |
| Commitment | A few days a week, ongoing | Project-based | Full-time loaded headcount |
| Time to active | 1-2 weeks | Weeks to scope | 3-6 months search and ramp |
| Cost structure | Fraction of a full-time leader; monthly fee | Project fees | $150K-$230K loaded annually |
Common questions from founders, CMOs, and data leaders evaluating a fractional data science manager.
An agency builds a model for you; leading the function, shipping it to production, and keeping it healthy is left to you. A fractional data science manager is a senior leader who owns the data science function on an ongoing basis and is accountable for models that ship and earn. You get leadership embedded in your business, not a hand-off.
A senior leader works with you on an ongoing, part-time basis, typically a few days a week, for a fraction of a full-time cost. It suits teams that need senior data science leadership and continuity but not a full-time role.
Applied, and tied to revenue. Our operators are senior practitioners, not researchers. They lead the building of models that improve real decisions and ship to production. For pure research-ML problems we scope honestly and say when a different specialist fits better.
A monthly fee scoped to the days per week you need, a fraction of the loaded cost of a full-time leader, and far less than a permanent executive hire.
Directly. Chameleon Collective is a senior-only collective with no account-management layer. The fractional leader is the person owning your models and the decisions they drive.
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A fractional data science leader gives you senior data science direction without a full-time hire. When the work grows enough to warrant a permanent leader, our Recruit practice runs retained executive search for senior data science talent, with a short list in 14 to 21 days, fixed-cap retained search, and a 12-month replacement guarantee.
Get In Touch
Tell us what you need. We will route to the operator whose pattern fits.
Senior fractional data science leadership for Fortune 500 retailers, financial institutions, consumer brands, and growth-stage businesses, keeping predictive models, recommendation engines, and pipelines shipping and healthy.
Tell us what you want to predict or automate and how much senior leadership you need. We will route to the operator whose pattern fits.
Schedule a conversationSpotlight
A deeper read on a few of the operators above: who they are and what they bring.
Data Scientist - CRM
Vu is a data scientist with more than 20 years building the models that run businesses, with deep work across UK retail (recommendation engines for Harvey Nichols and the British retailer made.com). A Partner at Chameleon Collective with genuine UK market experience. As a fractional Data Science Manager he runs the operation a few days a week, owning the models in production, the pipelines, and the data scientists who build them, remote-first with UK organisations, for a fraction of a full-time hire.
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Data, Analytics & Data Science
Yulian is a data scientist and analytics leader with 14+ years and a Master's in Data Science from Northwestern University. He specialises in predictive modelling, customer analytics, and AI: a customer data platform for The Container Store that unlocked $75 million, a marketing analytics sandbox for Lowe's that generated $150 million in six months, and optimisation models for Bergdorf Goodman that improved marketing ROI 11%. He is available on a fractional, remote-first basis to help run the data science operation for UK organisations.
View profileFeatured Case Study
When specialist brands move from regional strength to national scale, growth is rarely just a logistics problem. Expansion introduces new competitive sets, broader consumer expectations, and the risk of over-indexing on a core audience that doesn’t translate nationally. This project supported a premium regional coffee brand preparing to scale nationally by leveraging its parent company’s logistics and distribution network. The challenge was to understand who to grow with , and how to prioritise audiences without diluting brand equity. Objectives Identify the primary drivers and motivations of existing and potential coffee consumers Move beyond demographic targeting to uncover attitudinally distinct growth audiences Understand how values, behaviours, and preparation habits shape brand preference Provide clear acquisition and positioning guidance for national expansion Methodology Quantitative & Qualitative Research US nationally representative sample 4,237 respondents Segmentation & Modeling using R This approach ensured segments were grounded in how people actually consume and choose coffee , rather than abstract lifestyle labels. Strategic Implications This segmentation reframed national growth as a portfolio of acquisition strategies , not a single target audience. Key implications included: Prioritising segments aligned with brand strength rather than chasing raw volume Tailoring messaging, formats, and channels to distinct motivational drivers Identifying where national expansion should protect premium equity versus broaden accessibility Using segmentation to inform innovation, pricing, and distribution decisions
Chameleon Collective conducted quantitative and qualitative research with a US nationally representative sample of 4,237 respondents. Using segmentation and modeling in R, the team moved beyond demographic targeting to uncover attitudinally distinct growth audiences. This approach ensured segments were grounded in how people actually consume and choose coffee, rather than abstract lifestyle labels.
This segmentation reframed national growth as a portfolio of acquisition strategies, not a single target audience. The work enabled the brand to prioritise segments aligned with brand strength rather than chasing raw volume, tailor messaging and channels to distinct motivational drivers, and identify where national expansion should protect premium equity versus broaden accessibility. The segmentation informed innovation, pricing, and distribution decisions for the national rollout.
“A premium regional coffee brand preparing to scale nationally faced the real risk of expansion: over-indexing on a core audience that would not translate nationally. The work used attitudinal segmentation to answer who to grow with and how to prioritise audiences. That is the kind of senior, ship-it data science a leader brings to the function: a model that answers a business question leadership could not answer on instinct.”
Real results from fractional marketing leadership engagements.
