{"id":2077583,"url":"https://alion.io/job/lula-senior-data-scientist","title":"Senior Data Scientist","company":{"id":57348,"name":"Lula","domain":"lula.co.za","url":"https://alion.io/company/lula","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cape Town, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":36000,"max_usd":65000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"DVC","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-08T06:43:33Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-09T22:46:35Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"ROLE PURPOSE\nAs a Senior Data Scientist at Lula, you will turn data into decisions across the business. You will work with teams in product, operations, finance, marketing and credit to frame problems, build analyses and models, and get the results into the hands of the people who act on them.\nYour focus is practical data science with a strong machine learning edge: finding the signal, proving it with sound statistics, and making sure the models we rely on run reliably in production. You will work alongside our data and analytics engineers, set the standard for how data science is done at Lula, and help grow small and medium enterprises in South Africa through better use of data.\n\nKEY RESPONSIBILITIES\nPartner with stakeholders to translate business questions into analytical problems, and communicate findings and recommendations clearly to technical and non-technical audiences.\nOwn the productionisation of machine learning models, including reproducible pipelines, versioned code, automated testing and deployment, working with data engineers and analytics engineers.\nMonitor models in production for performance, drift and data quality, and drive retraining, remediation and improvement when they degrade.\nBuild, validate and maintain predictive models (e.g. classification, regression, forecasting, segmentation).\nExplore, clean and analyse large datasets to uncover trends, drivers and opportunities.\nDesign and analyse experiments (A/B tests, pilots) and measure the impact of business initiatives.\nBuild dashboards, reports and reusable analysis assets that make insights self-serve.\nHelp raise the standard for how data science is done at Lula\nContribute to data quality, documentation and good practice across the data team.\nTHE SKILLS AND EXPERIENCE WE’RE LOOKING FOR\nA numerical or quantitative degree (e.g. mathematics, statistics, data science, computer science, engineering, economics, finance)\n3-5 years' experience in a data science or advanced analytics role\nProven experience productionising machine learning models and monitoring them once live (performance, drift, data quality)\nExperience building and evaluating machine learning models on real-world data\nAt least 3 years' experience actively using Python in data analysis or modelling (pandas, scikit-learn or similar)\nAt least 3 years' experience actively using SQL for data analysis\nExperience applying software engineering practices to data science work (Git, code review, automated testing)\nExperience in FinTech, lending or small business environments, or a strong interest in the space\nAdvantageous\nFamiliarity with MLOps tooling such as MLflow, DVC or similar (experiment tracking, model registry, monitoring)\nExperience with dbt and CI/CD (e.g. GitHub Actions)\nExposure to cloud data platforms (Azure, AWS or GCP) and cloud data warehouses (e.g. Snowflake)\nExperience with notebooks and collaborative analytics tools (e.g. Hex)\nExposure to Docker, workflow orchestration or infrastructure as code\nExperience mentoring or technically guiding other data professionals\n\nOUR TECH STACK\nAzure (Functions, databases, blob storage)\nDBT\nSnowflake\nAirflow\nAirbyte\nEvent 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