ideally) Advanced knowledge of statistical modelling and machine learning, with relevant experience in the use of Python libraries (e.g., scikit-learn, xgboost, tensorflow, pymc3, statsmodels) and R equivalents Experience generating reports, dashboards, and data visualisations SQL experience as well as experience working with relational databases Excellent presentation, documentation, time management More ❯
non-technical stakeholders through effective data visualisation and building of reporting frameworks Comfortable with Python data science libraries such as pandas, scikit-learn, numpy, statsmodels Strong SQL experience including analytic functions, performance tuning, data wrangling Ability to work collaboratively and proactively in a fast-paced environment alongside scientists, engineers and More ❯
to non-technical stakeholders Strong programming experience in python (R, Python, C++ optional) and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain) Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal More ❯
and SQL, with the ability to query databases and manipulate large datasets. Proficiency in key Python libraries for data science, including Pandas, Scikit-learn, Statsmodels, NumPy, SciPy, Matplotlib, TensorFlow, and Keras. Solid understanding of machine learning techniques, such as clustering, tree-based methods, boosting, text mining, and neural networks. Expertise More ❯
use a range of technologies internally, familiarity with open-source tools is highly valued. Languages & Libraries: Python, R, Pandas, NumPy, scikit-learn, XGBoost, LightGBM, statsmodels NLP & ML Frameworks: spaCy, Hugging Face Transformers, TensorFlow or PyTorch (where applicable) Data Engineering & Pipelines: dbt, Airflow, SQL, Spark, Dask Visualisation: Plotly, Seaborn, Matplotlib, Dash More ❯
use a range of technologies internally, familiarity with open-source tools is highly valued. Languages & Libraries: Python, R, Pandas, NumPy, scikit-learn, XGBoost, LightGBM, statsmodels NLP & ML Frameworks: spaCy, Hugging Face Transformers, TensorFlow or PyTorch (where applicable) Data Engineering & Pipelines: dbt, Airflow, SQL, Spark, Dask Visualisation: Plotly, Seaborn, Matplotlib, Dash More ❯
product-focused roles. Deep understanding of LTV modeling, forecasting, and experimental design. Proficiency in Python for data analysis and modeling (e.g., pandas, scikit-learn, statsmodels). Advanced SQL skills and experience working with large datasets in modern data environments. Experience working with cross-functional growth or marketing teams. Competent user More ❯
modeling, forecasting, and experimental design. Comprehension of A/B testing methodologies Proficiency in Python for data analysis and modeling (e.g., pandas, scikit-learn, statsmodels). Strong SQL skills and experience working with large datasets in modern data environments. Experience working with cross-functional growth or marketing teams. Competent user More ❯
tools and packages where appropriate. Desired Skills & Experience: Core Technical Skills Expert in Python, SQL, and modern data science toolkits (e.g. scikit-learn, XGBoost, statsmodels). Solid grasp of dbt for data transformation. Experience with modern cloud data stacks - Snowflake, BigQuery, Redshift, etc. Comfortable working in agile environments with tools More ❯
in probability and statistics. Experience developing code collaboratively and implementing solutions in a production environment. Proficiency with data manipulation and modelling tools - e.g., pandas, statsmodels, R. Experience with scientific computing and tooling - e.g., NumPy, SciPy, Matlab, etc. Self-driven with the capability to efficiently manage projects end-to-end. Experience More ❯
insights. Capability to manage projects end-to-end and produce good outcomes without much supervision. Proficiency with data manipulation and modelling tools - e.g., pandas, statsmodels, R. Experience with scientific computing and tooling - e.g., NumPy, SciPy, R, Matlab, Mathematica, BLAS. Degree in Statistics, Mathematics, Physics or equivalent. Bonus: Experience implementing solutions More ❯
insights. Capability to manage projects end-to-end and produce good outcomes without much supervision. Proficiency with data manipulation and modelling tools - e.g., pandas, statsmodels, R. Experience with scientific computing and tooling - e.g., NumPy, SciPy, R, Matlab, Mathematica, BLAS. Degree in Statistics, Mathematics, Physics or equivalent. Bonus: Experience implementing solutions More ❯
do Interrogate large datasets in SQL to analyse depot efficiency, delivery timelines, stock levels, and service bottlenecks Use Python (Pandas, NumPy, scikit-learn/statsmodels) to apply performance models or forecasting where needed Build and maintain some Power BI dashboards — but you won’t be buried in reporting Work closely More ❯
do Interrogate large datasets in SQL to analyse depot efficiency, delivery timelines, stock levels, and service bottlenecks Use Python (Pandas, NumPy, scikit-learn/statsmodels) to apply performance models or forecasting where needed Build and maintain some Power BI dashboards — but you won’t be buried in reporting Work closely More ❯
do Interrogate large datasets in SQL to analyse depot efficiency, delivery timelines, stock levels, and service bottlenecks Use Python (Pandas, NumPy, scikit-learn/statsmodels) to apply performance models or forecasting where needed Build and maintain some Power BI dashboards — but you won’t be buried in reporting Work closely More ❯
do Interrogate large datasets in SQL to analyse depot efficiency, delivery timelines, stock levels, and service bottlenecks Use Python (Pandas, NumPy, scikit-learn/statsmodels) to apply performance models or forecasting where needed Build and maintain some Power BI dashboards — but you won’t be buried in reporting Work closely More ❯
warrington, cheshire, north west england, United Kingdom
SF Technology Solutions
do Interrogate large datasets in SQL to analyse depot efficiency, delivery timelines, stock levels, and service bottlenecks Use Python (Pandas, NumPy, scikit-learn/statsmodels) to apply performance models or forecasting where needed Build and maintain some Power BI dashboards — but you won’t be buried in reporting Work closely More ❯
