{"id":2548089,"url":"https://alion.io/job/job-mail-senior-data-scientist-10","title":"Senior Data Scientist","company":{"id":701519,"name":"Job Mail","domain":"jobmail.co.za","url":"https://alion.io/company/job-mail","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":true,"listed_via":"jobmail.co.za","ats_vendor":"Schema","truth_index":{"grade":"C","score":65,"open_postings":6887,"ghost_share":0.488,"stale_share":0,"repost_share":0.711,"time_to_fill_p50_days":7,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["South 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next-best-action, next-best-product, customer value, retention, and engagement models.\nDevelop risk, collections, fraud, and operational models to improve business performance and decision-making.\nBuild and maintain reusable feature pipelines using Databricks Feature Engineering and Delta tables.\nDefine, develop, and govern reusable enterprise features for inclusion in the Databricks Enterprise Feature Store.\nEstablish feature definitions, quality controls, lineage, and monitoring standards across business domains.\nPerform feature engineering, feature selection, and feature importance analysis to improve model performance and reusability.\nDevelop optimisation and decisioning models to support customer engagement, product recommendations, and operational strategies.\nConduct exploratory data analysis, statistical analysis, hypothesis testing, and model validation using Databricks notebooks and workflows.\nPerform model tuning, calibration, challenger model development, and performance benchmarking.\nMonitor model performance, stability, drift, and business outcomes, recommending improvements where required.\nDevelop Generative AI use cases where appropriate, including customer support, document intelligence, and knowledge-based assistants.\nCollaborate with Machine Learning Engineers to productionise models and features within enterprise platforms and decisioning systems.\nProduce model documentation, validation reports, and governance artefacts in line with model risk management requirements.\nTranslate business challenges into analytical solutions and actionable recommendations for stakeholders.\nMentor junior Data Scientists and contribute to modelling standards, reusable frameworks, and analytical best practices.\nWhat we are looking for:\nA qualification in Computer Science, Data Science, Engineering, Mathematical Statistics, Actuarial Science, Mathematics, Econometrics, or a related quantitative field.\nA Master’s degree or Doctorate will be advantageous.\nStrong experience developing and deploying Machine Learning models using Databricks.\nMinimum 4 years’ experience in Data Science, Machine Learning, or a related quantitative field, with hands-on experience developing and deploying Machine Learning models.\nStrong understanding of feature engineering, feature selection, and feature optimisation techniques.\nExperience building and governing reusable enterprise features within Feature Stores.\nHands-on experience with Databricks Notebooks, Workflows, MLflow, Delta tables, and Databricks Feature Engineering.\nExperience using MLflow for model tracking, experimentation, and model governance.\nExperience developing customer propensity, risk, fraud, retention, and optimisation models.\nStrong knowledge of statistics, Machine Learning, predictive and prescriptive analytics, and decision science methodologies.\nExperience working with large-scale customer, behavioural, transactional, and digital datasets.\nExperience designing model monitoring and performance measurement frameworks.\nKnowledge of Generative AI, Large Language Models (LLMs), prompt engineering, and applied AI use cases.\nExperience with experimentation, hypothesis testing, model validation, tuning, calibration, and performance benchmarking.\nPlease note that if you do not hear from us within 3 weeks, consider your application unsuccessful.\nPlease note that most of our positions are remote; however, candidates should reside within travelling distance of the client site, as the working arrangements may change depending on business 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