Hire Data Scientists who ship models that matter

A data scientist uses statistics, experiments and machine learning to help a business make better decisions under uncertainty. They work out whether a question can be answered with data, build the simplest method that works, measure whether it helped, and explain the result honestly.

In practice, a data scientist:

• Frames the problem: what decision changes, what does being wrong cost, and is ML even the right tool?
• Designs and analyses experiments such as A/B tests, pricing tests and rollouts.
• Builds predictive models for forecasting, churn, risk, recommendations or fraud, depending on your business.
• Works with data engineers and ML/platform teams to get models into production and monitor them.
• Communicates confidence, failure modes and trade-offs to product and leadership.

You probably need a data scientist when:

• You have a clear decision that a forecast or prediction would improve, such as demand, churn, credit risk or pricing.
• You're running experiments but nobody can say with confidence whether they worked.
• A product feature depends on a model: ranking, recommendations, scoring or detection.
• Your analysts are being asked questions that need statistics, not just SQL.
• You have enough reliable data to learn from. If you don't yet, start with a Data Engineer.

If you mainly need dashboards and answers to business questions, a Data Analyst is usually the better and faster hire.

Problem first, algorithm second. A strong data scientist asks what decision the model changes, what data is available and what "good enough" means before reaching for the newest architecture. Sometimes the answer is a better metric or a simpler rule, and they say so. They're not library tourists.

Strong Python and statistics judgment. They write code and choose methods that hold up in review, not demo notebooks that fail on next week's data. They understand distributions, bias, variance and why the baseline matters.

Experimentation discipline. They design tests with clear hypotheses, avoid leakage and report results against baselines. They know what "lift" actually means for the business, and they don't tell a model story that can't be measured.

Modelling craft with production awareness. Offline metrics matter, and so do latency, drift, monitoring and who will own the model. They partner with data engineering and ML platform teams. They don't throw a pickle file over the wall.

Communicates uncertainty. Hiring managers need someone who can say "we don't know yet" and put numbers on the risk. Carbon's vetting looks for that judgement, not only framework skills.

Working style, assessed rather than claimed. Every vetted profile includes a working-style profile, built from 12 work scenarios. It shows collaboration, ownership and how someone handles vague briefs.

What to probe in interviews

• "Tell me about a model you decided not to build. Why?"
• "How did you know your last experiment result was real?"
• "Your model's accuracy dropped after launch. Walk me through what you check."

Warning signs

• CVs that are dashboard work with a "Data Scientist" title.
• Impressive offline metrics with no idea how the model was used or monitored.
• "AI" on everything, with no baseline, success metric or data plan.

These are examples, not requirements. We match the brief. Deep learning and GenAI only come in when your role asks for them; we don't turn every data science hire into an "AI" hire.

• Core: Python (pandas, NumPy, scikit-learn), SQL, R where teams use it.
• Statistics and experiments: hypothesis testing, regression, causal inference basics, A/B testing tools.
• ML: gradient boosting (XGBoost, LightGBM), time-series forecasting, classification and ranking.
• Deep learning and GenAI (when the role needs it): PyTorch, TensorFlow, LLM APIs, retrieval and evaluation.
• Production: MLflow, Databricks, SageMaker, Vertex AI, Azure ML, Docker, monitoring for drift.
• Data: warehouse SQL, feature pipelines built with data engineering partners.

There are two ways to use carbon.talent. Both use the same profile.

Option 1: Post a role

1. Create your role. Title, stack, seniority, engagement type (full-time, contract or fractional), location and overlap hours. Posting is at no cost.
2. Match against Carbon’s talent. When you're ready, you compare the role with data scientists who have already been assessed on carbon.talent.
3. Review the profile. Vetted profiles come with a vetting report, a short video intro and a working-style profile. You also get an interview guide tailored to your role and the candidate.
4. Interview and hire. Interview fewer, better people. Agree the rate, draft the contract and sign on the platform.

Option 2: Test a candidate you found Already found a data scientist on LinkedIn, through your own network? Before you make an offer, send a SQL or Python skills test or the real-world data experience test, then review the report. The real-world data experience test is on every plan; SQL and Python tests are on paid plans. Add a working-style profile and a short video.

A CV is self-reported. Job titles mean different things at different companies, and remote hiring makes it harder to be sure who you are talking to. When a hiring process starts from CVs, your team does the checking in interviews, one expensive hour at a time.

carbon.talent profiles carry scenario-based vetting, a working-style profile and a video intro.

Monthly plans are priced per company. A once-off hire fee of 10% of first-year salary applies (7.5% for subscribers), due at the candidate's start date. For contract hires, 10% of the contract fee for up to 12 months. The real-world data experience test, working-style profile and video are included on every plan. Connect your AI assistant (MCP) on Team and Scale. SQL and Python skills tests are on paid plans. Webhooks and ID checks are on Team and Scale.

carbon.talent is a global hiring platform. Companies post roles from anywhere, and you choose where your hire is based and which hours they overlap with your team.

Hire where your work happens. Set the country, time zone overlap and currency on each role, and review people who fit.

• Engagement. Hire full-time, on contract or fractional through the same platform.

Hiring locally? The same process works for on-site and hybrid roles in any city. Location is a setting on your role, not a separate product.

Data scientist salaries vary widely by country, seniority, specialism and sector. Production ML and deep domain experience usually command more. Salary is agreed between you and the professional, and it is separate from the carbon.talent platform.

United States: for current U.S. wage ranges for data scientists, see the U.S. Bureau of Labor Statistics Occupational Outlook Handbook entry for Data Scientists. Benchmark pay against the person's market and seniority; we do not quote third-party figures here.