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Data Science Professional

2 weeks ago

Pretoria, Gauteng, South Africa Absa Group Full time

Empowering Africa's tomorrow, together…one story at a time. With over years of rich history and strongly positioned as a local bank with regional and international expertise, a career with our family offers the opportunity to be part of this exciting growth journey, to reset our future and shape our destiny as a proudly African group.

Job Summary

Leverage deep data science expertise in advanced statistics, data wrangling, data mining, data analysis, feature engineering & predictive modeling, storytelling, distributed computing & data visualisation, machine learning tools & data intuition to define, build, operationalize & continuously improve data solutions that deliver relevant, quality assured, accurate & commercially impactful data to the business.

Key Responsibilities
  1. Participate in design thinking processes to determine & confirm hypotheses and priority questions / data challenges & related metrics to be solved for.
  2. Translate business questions to be solved into data requirements & define a data solutions to deliver against these requirements.
  3. Proactively partner with the data engineering team to refine the data requirements deliver raw data to Data Science teams for interpretation & analysis.
  4. Design fit for purpose data interpretation & analysis approaches & create customized data models, algorithms, machine learning tools and recommendation engines to achieve the desired business outcomes.
  5. Use advanced data science skills to mine & interpret data including advanced statistics, data wrangling, data mining, data analysis, feature engineering & predictive modeling, distributed computing, machine learning tools & data intuition.
  6. Analyse & interpret complex data sets.
  7. Apply quality assurance frameworks to test model & analysis techniques (e.g. algorithms, models) & overall data quality.
  8. Produce business insights and recommendations based on data analysis & modeling concluded & where relevant with knowledge and experience of e.g. Databricks, Python, Hadoop, Apache Spark.
  9. Use storytelling and data visualization techniques to maximize impact & deliver a user friendly product to business.
  10. Contribute to the consolidation of data solutions into viable end products (in the language of business) that can be leveraged on an ongoing basis e.g. dashboards, reports etc.
  11. Present data analysis (trends, insights, forecasts) & findings to business & show tangible business impact to be derived from the data science process.
  12. Facilitate peer reviews & feedback on data solutions.
  13. Refine data analysis based on business & peer reviews.
  14. Contribute to the assessments of the effectiveness and accuracy of new data sources & data gathering techniques.
  15. Promote data literacy with your business stakeholders by sharing best practices and showing tangible business impact & recommendations as a direct result of the data solutions provided.
  16. Stay ahead of the curve on data science trends & leading practice data science tools and techniques & transition the organisation to advanced methods for the continuous optimization of data.
  17. Develop and retain models on customer behavior, retention and segmentation.
  18. Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization.
  19. Use machine learning tools and statistical techniques to produce solutions to problems.
  20. Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods.
  21. Conduct research from which you'll develop prototypes and proof of concepts.
  22. Look for opportunities to use insights / datasets / code / models across other functions in the organisation.
  23. Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
  24. Development of standardised modelling of behaviour / retention / value in customers.
  25. Maintenance of dictionaries: codes, dimensions, setting up tables etc.
  26. Run and compare models on a regular basis.
  27. Run Cluster specific scenarios to cover specific local issues.
  28. Business process maintenance (segmentation, primacy etc).
  29. Produce appropriate documentation and training material to enable handover to a BAU team.
  30. KVD Predictions.
  31. Using and deriving value from implemented models.
  32. Accountability: Risk & Governance
  33. Identify data risks and mitigate these (pre, during & post solution deployment / data delivery).
  34. Create business cases & solution specifications for various governance processes (if required).
  35. Apply data quality assurance frameworks and tools to guarantee data quality & data integrity (always) for specific data solutions.
  36. Contribute to risk, governance, compliance & broader regulatory processes as a data science expert (if & when required).
  37. Accountability: People
  38. Coach & mentor other data scientists.
  39. Conduct peer reviews, testing, problem solving within and across the broader team.

The following degrees and / or subjects may be particularly useful: Data science, Industrial engineering, Mathematics, Statistics, Actuarial science, Business mathematics and informatics.

Educational qualifications include a Bachelor's Degree in Information Technology.

Absa Group is an equal opportunity employer. In compliance with the Employment Equity Act 55 of, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.