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 a Devlyn senior reviewing 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.
MODELS THAT REACH A DECISION
WHAT THEY OWN
High-value data science, tied to a business metric.
01
Forecasting & demand planning
Models on your sales, price, promo, and seasonality signals that cut forecast error and feed inventory, capacity, and financial planning.
02
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.
03
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.
04
Recommendation & personalization
Ranking, next-best-action, and cross-sell models that raise conversion, order value, and engagement without hand-tuned rules.
05
Fraud & anomaly detection
Supervised and unsupervised models on transaction streams that catch fraud and outliers while holding false positives down.
06
Analytics & decision support
A trusted metric layer, cohort and funnel analysis, and plain-language recommendations that turn messy data into decisions leaders act on.
SCOPE, MADE CLEAR
Where a data scientist fits, and where they don't.
"Data scientist" gets used as a catch-all, which is how projects end up mis-scoped. The clean map: a data engineer builds the pipes, a data scientist finds the insight and builds the model, an ML engineer runs it in production, and an AI engineer ships LLM-powered product on top.
Data Analyst
Reports what already happened, dashboards and descriptive analysis on structured data.
Data scientist: A data scientist predicts what happens next and what to do about it.
Data Engineer
Builds the pipelines, warehouse, and data quality that feed everything downstream.
Data scientist: A data scientist consumes that data to find the insight and build the model.
ML Engineer
Productionizes, scales, and maintains models as reliable services.
Data scientist: A data scientist designs and validates the model an ML/MLOps engineer then runs.
See that role →AI / LLM Engineer
Ships LLM-powered product features, RAG apps, copilots, and agents.
Data scientist: A data scientist owns the statistical and predictive modelling behind the numbers.
See that role →PRICING
Two levels, senior oversight on both.
Junior
$3,200 /month
or $20/hr on Time & Material
Scoped analysis, senior-reviewed
Senior
MOST HIRED$4,800 /month
or $30/hr on Time & Material
Owns the analytical strategy
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The paid one-week trial applies to every dedicated hire.
YOU NEED THIS HIRE IF
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
OUTCOMES YOU CAN MEASURE
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
DEVELOPER PROFILES
See the depth behind a useful shortlist.
Explore data scientist profiles with the modeling, domain, and decision evidence behind a useful shortlist.
Reemu
Senior Data Scientist
Senior data scientist, 13 years turning demand and sales signals into forecasts operations teams actually act on.
PythonSQLTime-Series ForecastingDemand Forecasting+8
Sagar
Senior Data Scientist
Senior data scientist with 9 years turning behavioral data into models and decisions, specializing in experimentation and causal inference.
PythonSQL+8
Urvin
Senior Data Scientist (Marketing Science)
data scientist specializing in marketing mix modeling and price elasticity, with 3 years turning commercial data into budget and pricing decisions.
Price Elasticity ModelingPythonSQL+7
Abhishek
Data Scientist, Marketing and Product Analytics
Junior data scientist focused on marketing and product analytics: churn, experimentation, and LTV.
PythonSQL
pandas
scikit-learn+7
Asha
Data Scientist
Data Scientist with 6 years of experience, specializing in multilingual retrieval and evaluation for legal technology.
PythonSQLscikit-learn
PyTorch+6
Shruti
Data Scientist
Data Scientist with 6 years of experience, specializing in model supply-chain security for enterprise software.
PythonSQLscikit-learnPyTorch+6QUESTIONS BUYERS ASK
Straight answers before you commit.
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 one week produce 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. On the 30-minute call we help you tell them apart and staff the right seat.
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, Devlyn provides a free replacement with a documented handover. Engagements are planned with overlap for your working hours, in English, every day.