{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Data Scientist”, “description”: “
Requirements
- At least 5 years’ experience in client‑facing data science roles with demonstrable impact on business outcomes
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline
- Strong proficiency in Python or R, including libraries such as pandas, scikit‑learn, NumPy, TensorFlow, or PyTorch
- Solid understanding of statistical analysis, hypothesis testing, and experimental design
- Hands‑on experience applying a range of supervised and unsupervised machine learning techniques (e.g., Random Forest, regression models, clustering methods)
- Proficiency with SQL and data warehousing technologies
- Ability to translate complex analytical findings into clear, practical business recommendations
- Strong problem‑solving skills and natural curiosity for exploring and understanding data
- (Desirable) Experience working with cloud platforms such as Azure, AWS, or Google Cloud
- (Desirable) Background in deploying machine learning models into production environments (MLOps experience is advantageous)
- (Desirable) Hands‑on experience with big‑data or distributed computing tools such as Spark or Databricks
- (Desirable) Familiarity with visualisation tools such as Power BI, Tableau, or Plotly
- (Desirable) Industry experience in sectors such as retail, finance, healthcare, or similar (customisable)
- Strong analytical and conceptual thinking
- Excellent communication and data‑storytelling capabilities
- Effective collaboration and stakeholder‑engagement skillsHigh attention to detail and commitment to data accuracy
What the job involves
- Partner with business stakeholders to identify and prioritise opportunities where data science can deliver measurable value
- Collect, clean, and transform structured and unstructured data from multiple internal and external sources
- Develop, test, and deploy predictive models and machine learning algorithms to address business challenges
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, anomalies, and key drivers
- Communicate insights and recommendations through clear storytelling, visualisations, and dashboards
- Collaborate with engineering teams to productionise models and ensure reliability, scalability, and ongoing performance
- Evaluate model accuracy and effectiveness, implementing continuous optimisation and tuning
- Stay up to date with emerging data science tools, methodologies, and industry best practices
- Perform sensitivity analysis to assess model robustness and variable impact
Company: Deepstreamtech
Location: London
Job Description:
Requirements
- At least 5 years’ experience in client‑facing data science roles with demonstrable impact on business outcomes
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline
- Strong proficiency in Python or R, including libraries such as pandas, scikit‑learn, NumPy, TensorFlow, or PyTorch
- Solid understanding of statistical analysis, hypothesis testing, and experimental design
- Hands‑on experience applying a range of supervised and unsupervised machine learning techniques (e.g., Random Forest, regression models, clustering methods)
- Proficiency with SQL and data warehousing technologies
- Ability to translate complex analytical findings into clear, practical business recommendations
- Strong problem‑solving skills and natural curiosity for exploring and understanding data
- (Desirable) Experience working with cloud platforms such as Azure, AWS, or Google Cloud
- (Desirable) Background in deploying machine learning models into production environments (MLOps experience is advantageous)
- (Desirable) Hands‑on experience with big‑data or distributed computing tools such as Spark or Databricks
- (Desirable) Familiarity with visualisation tools such as Power BI, Tableau, or Plotly
- (Desirable) Industry experience in sectors such as retail, finance, healthcare, or similar (customisable)
- Strong analytical and conceptual thinking
- Excellent communication and data‑storytelling capabilities
- Effective collaboration and stakeholder‑engagement skillsHigh attention to detail and commitment to data accuracy
What the job involves
- Partner with business stakeholders to identify and prioritise opportunities where data science can deliver measurable value
- Collect, clean, and transform structured and unstructured data from multiple internal and external sources
- Develop, test, and deploy predictive models and machine learning algorithms to address business challenges
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, anomalies, and key drivers
- Communicate insights and recommendations through clear storytelling, visualisations, and dashboards
- Collaborate with engineering teams to productionise models and ensure reliability, scalability, and ongoing performance
- Evaluate model accuracy and effectiveness, implementing continuous optimisation and tuning
- Stay up to date with emerging data science tools, methodologies, and industry best practices
- Perform sensitivity analysis to assess model robustness and variable impact
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Posted: May 19th, 2026