Intermediate Data Scientist

**Key accountabilities and decision ownership**:

- Lead projects with hands-on analysis and modelling, drawing from multiple of analytical methods to choose the right tool and right level of complexity appropriate for the cloud based technology challenges.
- Engage broadly with the organization to identify, prioritize, frame, and structure complex and ambiguous challenges, where advanced analytics projects or tools can have the biggest impact.
- Identify, communicate and resolve the cloud based platform operational challenges and leverage public cloud analytical & ML/AI capabilities.
- Articulate operational problem statements and questions and use mathematical techniques to arrive at an answer using data. Translate analysis results into data driven recommendations.
- Collect, analyse, and interpret large data sets using analytical, statistical, and programming skills.
- Discover information, patterns in huge amount of data and build models with the purpose of automating activities within the process to support various use cases.
- Write code using Python, R, Scala, Java and SQL in order to build analytics modules using big data architectures.
- Build prototypes to support use cases and showcase them to business in order to obtain their buy in.
- Periodically, review production models and optimise or decommission where required.
- Research, investigate and evaluate new technologies and methods to improve advance analytics and machine learning capabilities.

**Core competencies, knowledge and experience**:

- Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.
- Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
- Programming: Write computer programs and analyze large datasets to uncover answers to complex problems.
- Data storytelling: Communicate actionable insights using data.
- Analytical thinking. Find analytical solutions to abstract business issues.
- Inquisitiveness: Look beyond what’s on the surface to discover patterns and solutions within the data.
- Interpersonal skills: Communicate across a diverse audience across all levels of an organization.
- Hands-on experience in predictive modelling, report and dashboard design.
- Strong experience developing and implementing multiple data science methodologies to solve complex business problems (e.g. statistical analysis, operations research, machine learning and deep learning techniques, etc.).
- Solid experience in handling terabyte-sized data sets, diving into data to discover hidden patterns, using data visualization tools, using SQL and databases.
- Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge, educating them about our systems, as well as sharing insights and data-driven recommendations.
- Agile exposure, Kanban or Scrum

**Minimum experience**:

- 2+ years of relevant experience with a proven track record in a highly quantitative field (Machine Learning, AI, Computer Science, Data Science, Statistics, Mathematics, Operational Research, etc.).
- Preferred experience:

- 4+ years of overall IT experience with Big Data, Advance Analytics, Data Warehousing and Business Intelligence.

**Must have technical / professional qualifications**:
**Minimum qualifications**:

- Bachelor’s degree in Statistics, Mathematics, Operations Research, Computer Science or related field or equivalent professional experience as a Data Scientist.
- Relevant cloud certification at professional or associate level.

**Preferred qualifications**:

- Master’s degree in Statistics, Mathematics, Operations Research, Computer Science or related field or equivalent professional experience as a Data Scientist.