Looking for Data Scientist ? Here’s what to know

If you’re looking for a data scientist, chances are one of three things is happening:

• Your data is growing faster than your insights
• Leadership keeps asking for “AI” without a clear plan
• Your current dashboards tell you what happened, not why

A good data scientist can turn that chaos into models, experiments, and decisions. A bad hire will burn budget, over-engineer solutions, and leave you with messy notebooks nobody understands.

This guide walks you through what data scientists actually do, the skills you should look for, typical freelance rates, and how to scope a project so you hire the right data scientist person the first time.

What Does a Data Scientist Actually Do?

Job titles vary wildly, but in practical terms a data scientist typically:

• Frames business problems as data problems
  • “Which users are likely to churn?”
  • “What’s the best price to maximise profit?”
  • “Which leads should sales call first?”
• Collects and prepares data
  • Joins data from product, CRM, ads, web analytics, support, etc.
  • Cleans, deduplicates, and engineers features from raw events.
• Builds and validates models
  • From simple regression and classification models
  • To more advanced time-series forecasting, recommendation systems, or NLP.
• Runs experiments and simulations
  • A/B tests, uplift models, scenario planning.
• Translates results into decisions
  • Communicates trade-offs and uncertainty to non-technical stakeholders
  • Works with product, marketing, or ops to implement and monitor models.

Many also overlap with:

• Data engineering (pipelines, ETL/ELT, cloud infrastructure)
• Machine learning engineering (deploying models to production)
• Analytics (dashboards, KPIs, ad-hoc analysis)

When you’re hiring, it’s crucial to know which of these hats you actually need.

Data Scientist vs Data Analyst vs ML Engineer

You may not need a “data scientist” at all. You might need:

• A Data Analyst if your main need is dashboards, reporting, and insight from existing data.
• A Data Scientist if you want predictive models, optimisation, or advanced experimentation.
• A Machine Learning Engineer if your biggest pain is putting models into production and maintaining them.

Very roughly:

• Data Analyst: What happened? Why?
• Data Scientist: What will happen? What should we do?
• ML Engineer: How do we run this model reliably in production?

For smaller companies and startups, one freelance data scientist might cover more than one of these roles, but you should still be explicit about what you want them to focus on.

Core Skills to Look for in a Data Scientist

When reviewing portfolios and profiles (for example, on a marketplace like Twine), you’ll want to look beyond buzzwords.

1. Solid Statistical Foundations

They should be comfortable with:

• Hypothesis testing and confidence intervals
• Regression (linear, logistic), classification, clustering
• Experimental design and A/B testing
• Understanding bias, variance, and overfitting

If they can’t clearly explain p-values, confidence intervals, or why correlation isn’t causation in plain English, that’s a red flag.

2. Strong Programming Skills

Most data scientists work in:

• Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow)
• Sometimes R in research or certain industries
• SQL for querying warehouses (BigQuery, Snowflake, Redshift, etc.)

They don’t need to be software engineers, but they do need to write clean, reproducible code and work with version control (Git).

3. Data Wrangling & Pipelines

Real-world data is messy. Look for experience in:

• Joining datasets from multiple systems
• Handling missing values, outliers, and edge cases
• Building or working with pipelines/ETL tools
  (e.g., dbt, Airflow, Fivetran, Airbyte)

4. Machine Learning & Modelling

Depending on your problem, they may need:

• Classification and regression (e.g., churn prediction, lead scoring)
• Time series forecasting (e.g., demand, revenue, inventory)
• Recommendation systems (e.g., products, content)
• Natural language processing (e.g., support tickets, reviews)

Ask for specific examples: “Show me a project where you improved a key metric using a model. What was the impact?”

5. Business & Communication Skills

The best data scientists:

• Start with business questions, not algorithms
• Explain models and uncertainty in plain language
• Can say “this model isn’t worth deploying yet”
• Are comfortable pushing back on unrealistic expectations

Portfolio items that mention business impact (“reduced churn by X%”, “increased upsell revenue by Y%”) are far more valuable than long lists of tools.

Freelance Data Scientist Rates: What to Expect

Rates vary widely based on seniority, location, and project complexity, but here’s a realistic guide for current markets.

Note: These are typical ranges for freelance data scientists, not full-time salary equivalents.

Hourly Rates (Global View)

• Junior / Early-Career (0–2 years): ~$35–$60/hr
  Good for simpler models, data cleaning, and support work under guidance.
• Mid-Level (2–5 years): ~$60–$110/hr
  Can own end-to-end projects: defining the problem, modelling, and presenting results.
• Senior / Consultant (5+ years):~$110–$200+/hr
  Often combine technical depth with strong product or strategy experience.

Day / Project Rates

• Day rates commonly range from roughly £500–£900/day in the UK and $800–$1,500/day in the US for experienced data scientists/consultants.
• Project fees can range from $3,000–$10,000 for smaller scoped projects (e.g., churn model prototype) to $20,000–$50,000+ for complex, multi-month engagements (e.g., recommendation system, end-to-end forecasting pipeline).

Expect to pay more if:

• Your data is highly regulated (fintech, health, insurance)
• You need production-grade ML systems, not just analysis
• You’re working with real-time systems or at very large scale

How to Scope a Data Science Project (So You Don’t Burn Budget)

The fastest way to waste money on data science is to start with: “We want to use AI. Can you see what the data says?”

Instead, craft a brief around a specific decision or metric.

1. Define the Business Objective

Examples:

• “Reduce customer churn in our SaaS product by 10% in the next 6 months.”
• “Improve paid acquisition ROAS by 20% by better predicting high-LTV users.”
• “Forecast demand for the next 90 days to optimise inventory.”

This anchors the work in value, not vanity projects.

2. Describe the Data You Have

Include:

• Data sources (e.g., product database, CRM, Stripe, ads platforms)
• Rough volume (rows per month, years of history)
• Known issues (missing fields, multiple IDs, manual data entry)

You don’t need to clean everything first, but you do need to be transparent about its state.

3. Decide on Time Horizon & Constraints

Share constraints around:

• Deadline (e.g., “We present to the board in 8 weeks.”)
• Infrastructure (e.g., “We already use BigQuery and Airflow.”)
• Budget (e.g., “We’re expecting to spend around $10k.”)

Good data scientists design solutions within constraints; unclear constraints cause scope creep.

4. Start with a Discovery Phase

For bigger projects, it’s smart to start with a paid discovery sprint, typically 1–2 weeks. Deliverables might include:

• Data audit and feasibility assessment
• Clear problem definition and success metrics
• Recommended modelling approach
• Roadmap and estimated cost for the full project

You then decide whether to proceed with the same freelancer, adjust the scope, or pivot.

Red Flags When You’re Hiring a Data Scientist

Here are some warning signs to watch out for:

1. Model-first thinking
   They talk more about algorithms than your business problem.
2. No real-world examples
   They can’t describe past projects in terms of impact, just tech.
3. Resistance to documentation
   Good data scientists know that future maintainers matter.
4. Over-promising on timelines
   Complex models + messy data + limited access rarely equals “done in 3 days”.
5. Lack of version control or process
   If they’re not using Git or can’t explain how they keep experiments organised, you may struggle to productionise anything.

How to Evaluate a Data Scientist’s Portfolio

When you look at candidates (for example, on Twine), don’t just glance at tools, read their case studies through a business lens.

Look for:

• Clear problem statements
  “The goal was to increase trial-to-paid conversion by predicting which users needed extra onboarding help.”
• Data & methods overview
  “We used product event logs and CRM data, engineered X features, and used gradient boosting to predict churn.”
• Results & decisions
  “The model identified 20% of users at high risk; interventions on this group reduced churn by 8%.”
• Ownership and role
  “I led model design and worked with engineers to deploy an API that served predictions daily.”

If their portfolio is entirely academic or Kaggle projects, that’s okay for junior roles, but for business-critical work, you’ll want evidence of production and impact.

Questions to Ask in an Interview

Here are practical questions that reveal how they think:

1. “Tell me about a project that didn’t go to plan. What did you do?”
   You’re looking for honesty, learning, and communication, not perfection.
2. “How do you decide if a model is good enough to deploy?”
   Listen for trade-offs, business metrics, and monitoring, not just accuracy numbers.
3. “What would you do in your first week on this project?”
   Strong candidates talk about understanding the business, data, and constraints.
4. “How do you communicate uncertainty to non-technical stakeholders?”
   Look for examples of simple explanations and visualisations.
5. “Can you walk me through your typical workflow, from raw data to insight/model?”
   You want a structured process, not chaos.

When to Hire via a Curated Marketplace

You can cold-message people on LinkedIn or sift through generic job boards, but it’s time-consuming and risky if you’re not a data expert.

Using a curated marketplace like Twine, you can:

• Post your project once and receive interest from vetted data scientists
• Filter by domain expertise
• Compare rates, experience, and portfolios
• Start with a small paid test project if you want before committing to a long engagement

That’s especially useful if you’re a startup, SME, or hiring manager who doesn’t live in the data science world and wants a safer way to choose.

Key Takeaways

If you’re looking to hire a freelance data scientist:

• Get clear on whether you need a data analyst, data scientist, or ML engineer, the overlap is real, but the focus is different.
• Expect freelance data scientist rates in the region of $60–$200+/hr, depending on seniority, location, and complexity.
• Scope around a business problem and metric, not a vague desire for “AI”.
• Start with a discovery phase to confirm feasibility and budget.
• Evaluate portfolios based on impact, not buzzwords, ask how their work changed decisions and metrics.