Hire Expert Data Scientists — Python, SQL, A/B Testing & Predictive Modelling
Hire pre-vetted data scientists specialising in EDA, statistical analysis, A/B testing and experimentation design, predictive modelling, customer analytics, Python, R, SQL (Snowflake, BigQuery, Redshift), and Tableau/Looker dashboards. Dedicated, part-time, or fixed-scope. Start in 3–5 business days.
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Data Science Skills & Expertise
EDA, statistical analysis, A/B testing, predictive modelling, SQL analytics, data visualisation, customer analytics, financial analytics, Python and R data science stack, and data strategy consulting.
Exploratory Data Analysis (EDA)
Rigorous exploratory analysis — data quality assessment (missing values, duplicate detection, outlier identification using IQR, z-score, and domain knowledge), distribution analysis (histograms, KDE, Q-Q plots), feature correlation analysis (Pearson, Spearman, Kendall for mixed data types), categorical variable analysis (chi-squared, Cramers V), time patterns (seasonality, trends, structural breaks), and hypothesis generation from data — EDA is where the real insight begins, before any model is trained.
Statistical Analysis & Hypothesis Testing
Applied statistical analysis for business decisions — hypothesis testing (t-test, Mann-Whitney U, chi-squared, ANOVA, Kruskal-Wallis for non-parametric comparisons), confidence intervals and uncertainty quantification, regression analysis (OLS, logistic, Poisson, Cox proportional hazards for survival analysis), Bayesian inference (PyMC, posterior distributions, credible intervals for more intuitive uncertainty communication), multiple comparison correction (Bonferroni, Benjamini-Hochberg FDR), and effect size reporting (Cohen d, odds ratio, relative risk).
A/B Testing & Experimentation Design
End-to-end A/B testing — experiment design (hypothesis, randomisation unit, primary and guardrail metrics, sample size calculation for target statistical power and significance level), experiment monitoring (novelty effect detection, sample ratio mismatch — SRM check, metric sensitivity analysis), statistical analysis (appropriate test selection, CUPED variance reduction using pre-experiment covariates, sequential testing for early stopping), and results interpretation (practical vs statistical significance, business recommendation with confidence interval reporting).
Predictive Modelling & Machine Learning
Applied predictive modelling for business forecasting — classification models (XGBoost, LightGBM, Random Forest, Logistic Regression for churn prediction, lead scoring, health risk stratification); regression models (demand forecasting, revenue projection, price elasticity); clustering (K-means, DBSCAN, Hierarchical clustering for customer segmentation); dimensionality reduction (PCA, UMAP, t-SNE); and time-series forecasting (Prophet, ARIMA, LSTM for sales, inventory, and demand planning). Feature engineering, cross-validation, and model interpretability (SHAP values, LIME).
SQL & Data Warehouse Analytics
Advanced analytical SQL across data warehouses — window functions (LAG, LEAD, RANK, NTILE, running totals, rolling averages, funnel analysis with conditional aggregation), complex joins and subqueries, CTEs for readable query architecture, cohort analysis, retention curves, funnel analysis, revenue attribution, user journey analysis; Snowflake, BigQuery, Redshift, and PostgreSQL; dbt for modular SQL transformations; and query optimisation (partition pruning, cluster keys, materialisation strategy).
Data Visualisation & Executive Reporting
Statistical visualisation and business communication — appropriate chart type selection for the data (distributions, comparisons, relationships, time series, compositions, geographic); Python visualisation (Matplotlib, Seaborn, Plotly for interactive); BI dashboards (Tableau, Looker, Power BI, Metabase) for business stakeholders; animated and custom D3.js visualisations; and written analytical narratives that translate statistical findings into actionable business recommendations with quantified impact and confidence intervals.
Data Science Tools & Technologies
Python (Pandas, NumPy, scikit-learn, statsmodels, PyMC), SQL (Snowflake, BigQuery, Redshift, dbt), R (ggplot2, survival, lme4), Tableau, Looker, Power BI, XGBoost, SHAP, Prophet, Plotly, and Evidently AI.
How to Hire a Data Scientist
Full-time dedicated data scientist, part-time specialist, or fixed-scope analytical project — structured for your data maturity and analytical objectives.
- Product analytics (A/B testing, funnel analysis, retention modelling)
- Customer analytics (segmentation, LTV, churn prediction)
- Revenue and financial analytics (MRR forecasting, pricing elasticity)
- Teams building their first data science capability
Available within 3–5 business days
- Ongoing A/B testing support for a product team
- Monthly business analytics and executive dashboards
- Seasonal demand forecasting and planning analytics
- Supplementing a BI team with statistical depth
Available within 2–3 business days
- Customer segmentation analysis (RFM + clustering)
- Churn prediction model with feature importance and SHAP analysis
- A/B testing framework design and first experiment analysis
- Cohort retention analysis and LTV model
Typical 2–8 week engagements
Our Data Scientist Hiring Process
From analytical question to first insight in 3–5 business days — domain-matched vetting, data audit, rigorous statistical analysis, and stakeholder-ready deliverables.
Tell us about the business questions you need to answer with data — are you trying to predict who will churn, understand which product features drive retention, design a pricing experiment, forecast next quarter demand, or segment your customer base? The more specific the business question, the better we can scope and match the right data scientist. Also tell us about your data — where it lives (warehouse, database, flat files), approximate volume, and what time period it covers.
Within 24 business hours we send 2–3 data scientist profiles matched to your domain and analytical problem type — with their experience in your industry (SaaS, e-commerce, fintech, healthcare), specific analytical methods (Bayesian A/B testing, survival analysis, causal inference), and tools (your data warehouse, Python, R). Not generic profiles — specialists matched to your data and business context.
Interview candidates on your specific analytical problems. Ask them to explain how they would design an A/B test for your product change, walk through their approach to a customer churn prediction, or describe how they would calculate statistical significance for your primary metric. Test real statistical reasoning — not just tool familiarity but conceptual understanding of when and why each method applies.
Before analysis begins, our data scientist audits your available data — quality, completeness, potential biases, and analytical limitations. We design the analytical framework upfront: which questions can be answered with current data, which require additional data collection, which require causal inference beyond correlation, and what the appropriate statistical methods are given your data characteristics.
Exploratory analysis (EDA), feature engineering, model development with rigorous cross-validation, statistical testing with appropriate test selection and multiple comparison correction, and model interpretability (SHAP values, feature importance, partial dependence plots). All code in Python or R, documented and reproducible. All findings documented with confidence intervals and clear statements of limitations.
Executive summary of findings with business impact quantification, visualisations appropriate for stakeholder audience (not raw statistical output), a stakeholder presentation with actionable recommendations, dashboards in Tableau/Looker/Metabase for ongoing monitoring, and a handover document explaining the methodology, limitations, and how to rerun the analysis as new data arrives. Knowledge transfer to your team.
What Our Clients Say
Analytics directors, customer success leads, and VP Supply Chain roles across the UK, AU, and US on hiring data scientists from 1Solutions.
1Solutions provided a data scientist who redesigned our entire A/B testing programme. She introduced CUPED variance reduction (cutting our required sample size by 30%), built a sample ratio mismatch detection system that caught 3 broken experiments we would have shipped, and wrote our experiment analysis documentation. Our experiment velocity tripled with the same team size.
We needed customer segmentation and churn prediction. 1Solutions sent a data scientist who did rigorous EDA on 3 years of transaction data, built an RFM + K-means segmentation with 7 clusters, and trained an XGBoost churn model with SHAP explanations. Our CRM team now acts on weekly churn risk scores. Churn in the at-risk segment dropped 18% in 90 days.
We hired a data scientist for demand forecasting — seasonal consumer goods, 200+ SKUs, 5 years of weekly sales data. He built a Prophet + LightGBM ensemble that reduced our MAPE from 24% to 9% vs the baseline naive forecast. Inventory holding cost dropped 12% in the first quarter with the new forecasts feeding directly into our supply chain planning tool.
Why Hire Data Scientists From 1Solutions
Statistical rigour, business question first, A/B testing depth, domain-specific methods, reproducible analysis, causation awareness, stakeholder communication, and data quality honesty.
Statistical Rigour, Not Data Theatre
Data science without statistical rigour produces confident wrong answers. Our data scientists understand sampling distributions, appropriate hypothesis tests for the data type, multiple comparison correction, effect sizes vs p-values, and causal inference limitations — they do not just report "statistically significant (p=0.04)" without context.
Business Question First
The best data science starts with a clear business question, not a dataset or model. Our data scientists translate business problems ("why are customers churning?") into analytical frameworks before touching the data — ensuring the analysis actually answers the question the business is asking.
A/B Testing Depth
Running experiments well requires more than a t-test calculator. Our data scientists design experiments with the correct statistical power, monitor for sample ratio mismatch and novelty effects, apply variance reduction (CUPED), use sequential testing for early stopping, and report effect sizes with confidence intervals — not just p-values.
Domain-Specific Analytical Methods
SaaS product analytics (cohort retention, LTV, funnel analysis) requires different methods than supply chain forecasting (ARIMA, seasonal decomposition, intermittent demand), financial analytics (time-series risk models), or healthcare (survival analysis, clinical trial statistics). We match your domain with appropriate analytical expertise.
Reproducible, Documented Analysis
Analysis that only the original data scientist can reproduce is a liability. Our data scientists write clean, documented Python or R code in version-controlled notebooks with clear methodology sections, so your team can rerun, modify, and extend the analysis as new data arrives.
Causation vs Correlation Awareness
Correlation does not equal causation. Our data scientists are explicit about when correlation-based analysis applies (EDA, segmentation) vs when causal inference methods are needed (A/B testing, difference-in-differences, propensity score matching) — and when the data simply cannot answer the causal question being asked.
Communication to Non-Technical Stakeholders
Statistical findings that stay in a notebook do not change decisions. Our data scientists translate findings into business language: quantified impact with confidence intervals, visualisations appropriate for the audience, and a clear recommendation with stated assumptions. They write for the decision-maker, not the statistician.
Data Quality Honesty
Garbage in, garbage out. Our data scientists audit data quality before analysis, flag sampling biases, selection effects, and missing data mechanisms that affect conclusions — and are explicit about what the data cannot tell you, not just what it can.
Hire a Data Scientist Today
Share your data science questions — the business questions you need answered, your data sources, current analytical capability, and timeline — and we will shortlist pre-vetted data scientists within 24 business hours matched to your domain and analytical problem type.
Shortlisted data scientists within 24 business hours
Domain-matched — SaaS analytics, e-commerce, fintech, supply chain
Full-time, part-time, or fixed-scope — flexible engagement
Data audit before analysis begins
5-day rapid replacement guarantee
Tell Us Your Data Science Questions
Hiring Data Scientists — FAQ
Common questions about hiring data scientists — what they do, statistical methods, A/B testing, tools, big data, visualisation, and data warehouse compatibility.
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