Hire a Data Scientist
Turns your data into models and decisions, not just dashboards.
A dedicated data scientist who frames the business question, prepares the data, builds the model, and proves the lift — forecasting, churn, experimentation, recommendation, and fraud — with senior oversight on everything that informs a decision.
30-minute fit call with an engineering lead. No hard sell, no deposit, and no change to the published rates.
What they own: high-value data science, tied to a business metric.
Forecasting & demand planning
Models on your sales, price, promo, and seasonality signals that cut forecast error and feed inventory, capacity, and financial planning.
Churn prediction & retention
Risk scores on every account from usage, billing, and engagement, so retention spend lands on the high-value customers actually about to leave.
Experimentation & causal inference
A/B tests done right: power and sample size, variance reduction, and causal methods when clean randomisation is not possible, so you ship changes that truly move the metric.
Recommendation & personalization
Ranking, next-best-action, and cross-sell models that raise conversion, order value, and engagement without hand-tuned rules.
Fraud & anomaly detection
Supervised and unsupervised models on transaction streams that catch fraud and outliers while holding false positives down.
Analytics & decision support
A trusted metric layer, cohort and funnel analysis, and plain-language recommendations that turn messy data into decisions leaders act on.
When to hire a Data Scientist, and what week one proves.
- ●You have data but no one turning it into decisions
- ●Your "data scientist" keeps building dashboards, not predictions
- ●Forecasts, churn, and pricing still run on gut feel
- ●A model got built once in a notebook and never reached production
- 01Completed a candid data-readiness assessment on your core tables
- 02Framed the target metric and offline holdout evaluation protocol
- 03Built and benchmarked the first baseline predictive model
- 04Quantified expected business lift and production deployment path
- ✓Decisions backed by models, measured against a holdout set
- ✓Forecasts, churn scores, and experiments your team trusts
- ✓A clear data-readiness assessment before anything is promised
- ✓Insight translated into plain-language recommendations
Where a Data Scientist fits, and where they don't.
From role brief to production evidence.
You are not buying a resume. You are choosing a specific engineer, then testing the match on committed work before making a longer decision.
- 01
Map the real gap
A 30-minute call with an engineering lead defines what the Data Scientist must own, the stack they inherit, and the evidence that will count as a successful trial.
- 02
Review matched profiles
Within 48 hours, you receive 2–3 role-matched profiles. Review work history, technical evidence, certifications, and availability before choosing whom to interview.
- 03
Interview against the work
We help turn your current failure cases into practical interview scenarios. You choose the engineer; no profile moves forward without your approval.
- 04
Prove fit in one paid week
The engineer works in your repo or approved data environment. You judge real output, communication, and technical decisions before any month-to-month continuation.
No deposit, no unpaid test project, and no long-term contract required. Trial work is paid at the published rate and belongs to you.
Pick the level, keep the senior oversight.
- ✓Executes scoped analysis and models designed with a senior
- ✓Data extraction, cleaning, EDA, baseline models, dashboards
- ✓Every notebook and model reviewed before it informs a decision
- ✓Frames the problem, picks the method, and defines how success is measured
- ✓Builds advanced models and ships business recommendations independently
- ✓Mentors your team, reviews work, and sets the technique standard
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The paid one-week trial applies to every dedicated hire.
Most teams add a second seat once the first proves out.
What teams ask before they shortlist.
Comparing multiple engineering seats? Browse all 9 dedicated AI & data roles or book a 30-minute fit call to map the right boundary.
Is our data ready for this?
Usually the first honest question. A large share of any data science project is preparing and cleaning data, so a good hire starts with a short data-readiness assessment — what exists, how clean it is, and what is realistic — before promising models. You get that assessment early, not after a month of surprises.
How long until a first model or proof of concept?
It depends on data maturity and how clearly the use case is scoped, but as a rough guide: GenAI/RAG proofs of concept often run 2–4 weeks, classical machine-learning PoCs 4–8 weeks, and data-engineering-heavy work longer. We scope the target and the data before the trial so the first week produces evidence, not setup.
Will the model actually reach production?
This is where most data science stalls — a model that lives in a notebook and never ships. We insist on a deployment and monitoring plan up front, and pair the data scientist with an MLOps or AI infrastructure engineer when the work needs a production path, model registry, and drift monitoring.
How are results measured?
Offline on a holdout set (accuracy, AUC, error), then online with a controlled A/B or holdout test that ties the model to a real business metric — revenue, churn, or margin — so you measure incremental lift, not just model scores. Monitoring then binds model metrics to business KPIs to catch silent degradation.
Data scientist, ML engineer, or AI engineer — which do I need?
A data scientist finds the insight and builds the model; an ML/MLOps engineer productionizes and scales it; an AI/LLM engineer ships LLM-powered product features like RAG apps and agents. If your question is "what does our data tell us and what should we do", you want a data scientist.
What about IP, replacement, and timezone?
NDA and IP assignment are signed before onboarding and everything produced belongs to you, including trial work. If the fit is wrong, we provide a free replacement with a documented handover. Engagements are planned with overlap for your working hours, in English, every day.
Role-specific hiring & engineering playbooks for Data Scientists.
How to Hire a Data Engineer (the AI Foundation)
How to Hire Dedicated Data Engineers for Warehouse Modernization and AI Readiness
7 Signs Your Snowflake or BigQuery Bill Is Bloated by Bad Data Engineering
8 Data Quality Gates That Must Run Before Records Reach Your Warehouse or RAG Index
Airflow vs. Dagster vs. Temporal: Choosing the Right Orchestrator for Data and AI Pipelines
How to Build a Change Data Capture (CDC) Pipeline for Real-Time AI and Analytics
Prefer to train your team first? Get the engineering field manuals.
Interview a Data Scientist this week.
Bring your stack, your failure cases, and your constraints. We'll send 2–3 vetted profiles within 48 hours, then use the paid one-week trial to prove fit in your environment.
30-minute fit call · No hard sell, no deposit, and no unpaid test project · NDA before onboarding · Free replacement · No lock-in