{"id":4016822,"url":"https://alion.io/job/magnus-consulting-data-scientist","title":"DATA SCIENTIST","company":{"id":4530971,"name":"Magnus Consulting","domain":null,"url":"https://alion.io/company/magnus-consulting","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Johannesburg, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AIOps","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CatBoost","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Interpretability","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Trino","optional":false},{"name":"XGBoost","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-11-10T10:25:48Z","employer_posted_date":null,"last_verified_at":"2026-11-10T10:25:48Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Introduction\nFunction / Division Consumer CVM and Loyalty\n\nReporting Line Senior Manager\n\nDuties & Responsibilities\n• Build and enhance personalised recommendation models for the personalised inbound product platform. • Develop models for customer propensity, product selection, ranking, pricing and affordability optimisation. • Evolve existing scoring approaches towards adaptive, data-driven ML and AI decisioning. • Develop customer-level features using usage, spend, recharge, purchase, engagement and response behaviour. • Develop optimisation approaches that balance customer relevance, affordability, conversion and commercial value. • Build, train, validate and optimise predictive and recommendation models using appropriate statistical and machine-learning techniques. • Develop learning-to-rank, experimentation, uplift and adaptive-learning approaches where appropriate. • Design robust control groups, champion/challenger tests and experimentation frameworks. • Establish appropriate measures of model performance, including predictive accuracy, lift, calibration and incremental commercial impact. • Build model explainability and interpretability into recommendation decisioning. • Monitor model performance, feature drift and model degradation over time. • Define modelling standards, review technical approaches and provide design assurance for work delivered by other data scientists. • Select fit-for-purpose algorithms and establish robust champion/challenger, back-testing and model validation approaches.\n\nData Analysis &\n\nInsight Generation • Perform advanced analysis (e.g., cluster profiling, cohort analysis, penetration/lift analysis) at scale using Spark/Databricks on large datasets (10M+ rows). • Generate business-friendly insights and recommendations from model outputs for stakeholders. • Query and manipulate data using SQL (Hive/Trino) and Python (pandas, PySpark). • Lead complex analytical investigations across multiple data domains and convert findings into clear commercial decisions and measurable actions. • Establish reusable analytical frameworks, curated features and quality checks that improve consistency and speed across the team\n\nMLOps &\n\nProductionisation • Own and lead the transition of models from development into production, including monitoring, regression testing and performance evidence. • Apply and enforce MLOps best practices (version control, experiment tracking, CI/CD, model monitoring) in partnership with the AI Ops function.\n\n• Track and report on the performance and drift of models deployed in production. • MLOps capability is mandatory: independently implement reproducible training pipelines, experiment tracking, model registration, automated testing and controlled deployment workflows. • Define production-readiness criteria covering data quality, model validation, explainability, security, documentation, rollback and operational support. • Establish monitoring for data drift, concept drift, model performance, service reliability and business outcomes, with clear alerting and remediation processes. • Drive model lifecycle governance, technical documentation and audit evidence in partnership with AI Ops, data engineering, architecture and risk stakeholders.\n\nCollaboration &\n\nCapability Building • Support upskilling initiatives (MLOps, coding standards, Agile/Scrum for data science) and mentor graduates/interns. • Provide technical leadership, coaching and structured peer review to data scientists, graduates and cross-functional delivery teams. • Lead solution design across data science, data engineering, MLOps, BI and architecture, resolving dependencies and technical risks early. • Improve team capability through\n\nDesired Experience & Qualification\nMinimum 3-year tertiary degree in a STEM field: Computer Science, Engineering, Mathematics, Statistics, Data Science or a related quantitative discipline.\n\n• Honours or Master's degree in Data Science, Statistics or a related field advantageous.\n\n• Fluent in English.\n\nExperience\n\n• 5-10 years' experience in data science / advanced analytics, including ownership of complex solutions from business problem framing through production deployment and value realisation.\n\n• Experience working with large-scale distributed data processing (Spark/Databricks) is highly advantageous.\n\n• Demonstrated hands-on MLOps experience and a track record of deploying and operating machine learning models in production are mandatory.\n\n• Experience with model governance, explainability, risk controls, data quality and production monitoring in an enterprise environment.\n\n• Strong experience engaging senior business stakeholders, defining measurable success criteria and translating model outputs into operational or commercial outcomes.\n\n• Demonstrated ability to mentor data scientists, improve engineering standards and influence technical direction without relying solely on formal authority.\n\n• Proven experience leading technical design, model review and delivery across multidisciplinary teams.\n\nTechnical Skills\n\nProgramming & Query Languages Python (pandas, PySpark), SQL (Hive, Trino)\n\nBig Data & ML Platforms Databricks\n\nMachine Learning K-Means/clustering, XGBoost, CatBoost, LightGBM, classification, regression, anomaly detection etc\n\nMLOps & DevOps MLflow, CI/CD pipelines, Azure DevOps, model monitoring & drift detection\n\nVisualisation & Reporting Power BI, Databricks\n\nBehavioural & Core Competencies\n\n• Strategic Thinking & Problem Solving - able to translate ambiguous business problems into structured analytical approaches.\n\n• Analytical & Innovative - rigorous, curious, and constantly looking for better ways to model and interpret data.\n\n• Communication & Business Storytelling - able to explain technical concepts and results in clear, business-friendly language for both technical and executive audiences.\n\n• Cross-Functional Collaboration - comfortable partnering with business, engineering and governance stakeholders.\n\n• Attention to Detail & Ownership - takes accountability for code quality, model accuracy and documentation.\n\n• Adaptability & Resilience - thrives in a fast-paced environment spanning multiple concurrent projects and priorities.\n\n• Technical Leadership & Judgement - sets direction, challenges weak assumptions and makes sound decisions under uncertainty.\n\n• Coaching & Capability Building - develops others through practical guidance, constructive review and meaningful delegation.\n\n• Commercial Acumen & Value Orientation - connects analytical work to measurable customer, revenue, cost, risk or experience outcomes.\n\n• Governance & Risk Ownership - anticipates model, data and operational risks and ensures appropriate controls are built into delivery\n\nPackage & Remuneration\nnegotiable","description_format":"text","description_chars":6770,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-11-10T12:57:53Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":25,"reasons":["seen:0","win:early"],"computed_at":"2026-11-10T15:32:04Z"},"pay":null,"html_url":"https://alion.io/job/magnus-consulting-data-scientist","json_url":"https://alion.io/job/magnus-consulting-data-scientist.json","meta":{"generated_at":"2026-11-10T15:32:04Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":1808,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-11T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4530971},"rest":"https://alion.io/mcp/rest/get_company?id=4530971"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmagnus-consulting-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmagnus-consulting-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmagnus-consulting-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/magnus-consulting-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmagnus-consulting-data-scientist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4016822}},{"tool":"search_jobs","arguments":{"company":4530971,"detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4016822},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}