Job Description

Contribute to the design and prototyping of analytic engines and services with a foundational understanding of machine learning, data mining, and information retrieval concepts

Job Responsibilities

• Implement AI and ML solutions and establish system operations and maintenance structures.
• Provide strategic and operational advice to the stakeholders regarding AI/ML infrastructure to serve current and future needs.
• Transform data science prototypes and optimise machine learning solutions for scalability and deployment into production.
• Design dynamic ML models and systems which have the ability to train, retrain themselves whenever required.
• Periodically evaluate the ML systems and ensure that end solutions are in alignment with corporate and IT strategies.
• Evaluate the variations in data distribution that affects the model performance.
• Visualise data for insights and perform statistical analysis, applying new insights to improve the model.
• Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
• Demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.
• Apply machine learning algorithms and libraries.
• Lead on software engineering and software design.
• Able to produce end to end designs (infrastructure, security, networks, etc) and work with other engineering leads to ensure all aspects of the data science solution is catered for.
• Communicate and explain complex processes to people who are not programming experts.
• Research and implement best practices to improve the existing machine learning infrastructure.
• Be able to design different analytics approaches for various problem types.
• Ensure Quality controls of own work and those of junior scientists.
• Understand current state of analytics more broadly and apply techniques from across industries.
• Work with large data sets, simulation/optimization and distributed computing tool.
• (Kubernetes, Map/Reduce, Hadoop, Hive, Spark, Gurobi, Arena, etc.)
• Map problems and quantify the impact of proposed measures.
• Develop best-in-class statistical models and algorithms.
• Conducts advanced statistical analysis.
• Apply advanced analytical techniques such as machine learning (ML) and artificial intelligence (AI) in order to derive business value.
• Keep abreast with latest tools and techniques.
• Understands business problems and designs end-to-end analytics use cases.
• Collaborates with model developers to implement and deploy scalable solutions.
• Develop complex models and algorithms that drive innovation throughout the organization.
• Ensure improvement of on-time performance and network planning,
• Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders.
• Develop and prioritize ML roadmaps.
• Ensure personal growth and enable effectiveness in performance of roles and responsibilities by ensuring all learning activities are completed, experience practiced, and certifications obtained and/or maintained within specified time frames.
• Enable skilling and required corrective action to take place by sharing knowledge and industry trends with team and stakeholders during formal and informal interaction.
• Support the achievement of the business strategy, objectives and values.
• Stay abreast of developments in field of expertise.
• Contribute to the Nedbank Culture building initiatives (e.g. staff surveys etc.).
• Participate and support corporate responsibility initiatives for the achievement of business strategy.

Key Responsibilities Continue

• Study and transform data science prototypes.
• Design machine learning systems.
• Research and implement appropriate ML algorithms and tools.
• Develop machine learning applications according to requirements.
• Select appropriate datasets and data representation methods.
• Run machine learning tests and experiments.

Essential Qualifications - NQF Level

• Matric / Grade 12 / National Senior Certificate
• Advanced Diplomas/National 1st Degrees

Preferred Qualification

• Preferably any Cloud (Azure, AWS), DEVOPS or Data engineering certification. Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist.

Type of Exposure

• ML Engineering
• Data warehousing
• Advance analytics
• Marketing analytics
• Financial analytics
• Presentations skills
• Predictive analytics
• Data mining
• Strategy formulations

Preferred Certifications

• Cloud (Azure, AWS), DEVOPS or Data engineering certification. Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist.

Minimum Experience Level

• 1-3 years' experience in a data science or cloud based role
• Portfolio of delivering projects successfully into production

Technical / Professional Knowledge

• ML Ops (Cloud Devops)
• ML Ops (ML engineering - running a Data Science platform)
• Deep knowledge of machine learning, statistics, optimization or related field
• Experience in Python(Must) and one additional language (SAS, Java Lua, Clojure, Scala, etc)
• Experience working with large data sets, simulation/ optimization and distributed computing tools (Kubernetes, Map/Reduce, Hadoop, Hive, Spark, etc.)
• Experience in end to end Use case delivery
• Ability to translate data narrative to business narrative
• Excellent written and verbal communication skills along with strong desire to work in cross functional teams
• Attitude to thrive in a fun, fast-paced start-up like environment

Behavioural Competencies

• Strong problem solving skills
• Good communication skills
• Ability to work in teams
• Decision Making
• Innovation
• Continuous Improvement

Please contact the Nedbank Recruiting Team at +27 860 555 566

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