Data Scientist

Job Family: Marketing, Communication, and Data Analytics

Application Development: Manage Others

Job Purpose: Lead in designing and building next-generation analytic engines and services, applying substantial expertise in machine learning, data mining, and information retrieval to drive impactful solutions and contribute to data-driven decision-making.

Job Responsibilities:

1. Development of statistical models and algorithms.
2. Conduct statistical analysis to gain insights from complex datasets, supporting data-driven decision-making efforts.
3. Offer insights and observations to stakeholders, identify trends, and measure performance.
4. Support the creation of value from data, assisting in translating data into meaningful business solutions.
5. Gain proficiency in financial services domain concepts and regulations to support the development of statistical models and AI / ML solutions tailored for financial applications.
6. Collaborate with experienced banking professionals to design and implement ML models that meet the unique requirements of financial institutions.
7. Contribute to shaping the organization's AI / ML strategy with the support of senior team members.
8. Participate in converting data science prototypes into scalable machine learning solutions for potential deployment.
9. Support the design of ML models and systems, considering adaptability and retraining capabilities under the guidance of experienced team members.
10. Participate in the assessment of ML system performance to ensure alignment with corporate and IT strategies, collaborating with experienced colleagues.
11. Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture.
12. Strong proficiency in programming tools (such as Python, R, etc.) for data manipulation, statistical analysis, and machine learning tasks is essential.
13. Familiarity with big data frameworks, such as Apache Hadoop or Spark, and have a willingness to learn and grow their expertise in handling and analyzing large-scale datasets.
14. Utilize machine learning algorithms and libraries with hands-on experience.
15. Support software engineering and design aspects of projects with mentorship from cross-functional teams.
16. Contribute to end-to-end designs with support from experienced team members.
17. Adapt communication for non-programming experts.
18. Stay informed about the latest tools and techniques, engaging in continuous learning.
19. Contribute to the evaluation of data distribution variations impacting model performance.
20. Apply foundational analytical techniques to support business value through ML and AI.
21. Familiarity with cloud computing concepts and basic experience in deploying data science solutions on cloud platforms.
22. Collaborate with the team, sharing ideas and insights.
23. Assist in the development of ML roadmaps.
24. Seek learning opportunities and contribute to knowledge-sharing within the team.
25. Contribute to the achievement of the business strategy, objectives, and values as a valuable team member.

People Specification:

Essential Qualifications - NQF Level: Matric / Grade 12 / National Senior Certificate.

Advanced Diplomas / National 1st Degrees.

Preferred Qualification: STEM Qualification: Computer Science, Econometrics, Mathematical Statistics, Actuary Science.

Minimum Experience Level: 3-7 years' experience in a statistical and/or data science role.

Deep knowledge of machine learning, statistics, optimization, or related field.

Experience with R, Python, Matlab is required; programming in C, C++, Java.

Experience working with large data sets, simulation/optimization, and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, Gurobi, Arena, etc.).

Excellent written and verbal communication skills along with a strong desire to work in cross-functional teams.

Attitude to thrive in a fun, fast-paced start-up-like environment.

Technical / Professional Knowledge:

1. Data Mining
2. Research and analytics
3. Data Tools
4. Data analysis
5. Statistical Analysis
6. Data structures
7. Presentation Skills
8. Supervised Learning
9. Unsupervised Learning
10. NLP
11. HyperParameter Tuning
12. Programming
13. Domain Knowledge
14. AI Ethics and Fairness
15. Decision Making
16. Customer Focus