TITRE DU POSTE

Data Scientist

DEPARTEMENT

Data & Analytics

LIEU D’AFFECTATION

Kinshasa

TYPE DE CONTRAT

CDI assorti d’une période d’essai de 6 mois

Job function*

Data & Analytics

Job Cluster

Data Monetisation

Job reports to*

Head, Data & Analytics

Job purpose description*

Assist in applying data mining techniques and conduct statistical
analysis to large, structured and unstructured data sets to understand and analyses phenomena. Model business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualization techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions.

Output group 1*

Client

Outputs and measures*

• Supports business integration through integrating model outputs into end-point production systems, incorporating business requirements and knowledge of best practices.

Output group 2*

Data

Outputs and measures*

• Assist the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organizations goals. Performs data pre-processing including data manipulation, transformation, normalization, standardization, visualization and derivation of new variables/features. Utilizes advanced data analytics and mining techniques to analyses data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders.
• Assists various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Assists analytics and insights across the organization by developing advanced statistical models and computational algorithms based on business initiatives.
• Codes, tests and maintains scientific models and algorithms and identifies trends, patterns, and discrepancies in data and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
• Use data profiling and visualization techniques using tools to understand and explain data characteristics that will inform modelling approaches. Communicate data information to business with respective stakeholders, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations under the supervision of data scientists.
• Utilizes the appropriate data storage and data mining tools to ensure value can be extracted from the sourced data. Mines data using state-of-the-art methods and enhances data collection procedures to include information that is relevant for building models.

Output group 3*

People

Outputs and measures*

• Liaise and collaborate with the Data Science Guild providing support to stakeholders in the department for its data centric needs. Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining/science outcomes. Presents findings and observations to team for development of recommendations.

Output group 4*

Product

Outputs and measures*

• Supports various mathematical, statistical, and simulation techniques to answer business questions within specific areas of focus. Develops modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Supporting reporting production ensuring sustainable and effective modelling solutions.
• Supports and implements operational IA plan, rules, methodologies and coding initiatives in order to ensure IA for remediation efforts. Support and implements the strategy for productionalising automation software so that it is accurate and well maintained.

Output group 5*

Technology & Architecture

Outputs and measures*

• Supports various mathematical, statistical, and simulation techniques to answer business questions within specific areas of focus. Develops modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Supporting reporting production ensuring sustainable and effective modelling solutions.
• Assists in building machine learning models from and utilizes distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.

Minimum qualification 1*

Type of qualification: First Degree
Field of study: Information Studies

Minimum qualification 2

Type of qualification: First Degree
Field of study: IT and Computer Sciences

Preferred qualification 1*

Type of qualification: Post Graduate Diploma
Field of study: Information Studies

Preferred qualification 2

Type of qualification: Post Graduate Diploma
Field of study: IT and Computer Sciences

Job Function

Job Cluster

Years

Experience Description

Technology

Technology Business Partnering

3-4 years

• Proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products.
• Experience in technical business intelligence.
• Knowledge of IT infrastructure and data principles.
• Project management experience.
• Experience in building models (credit scoring, propensity models, churns, etc.)

Data & Analytics

Data Monetization

3-4 years

• Experience in working with unstructured data (e.g. Streams, images)
• Understanding of data flows, data architecture, ETL and processing of structured and unstructured data.
• Using data mining to discover new patterns from large datasets.
• Implement standard and proprietary algorithms for handling and processing data.
• Experience with common data science toolkits, such as SAS, R, SPSS, etc.
• Experience with data visualization tools, such as Power BI, Tableau, etc.

Behavioural competency 1*

Competency Label: Adopting Practical Approaches
Competency Description: Adopting practical solutions with an emphasis on learning by doing. This competency requires individuals to utilize common sense when required. Ultimately, this competency is important in order to ensure that organizations implement feasible solutions.

Behavioural competency 2

Competency Label: Articulating Information
Competency Description: This competency is about effectively expressing ideas and concerns, giving presentations, explaining things to others as well as showing confidence in the interaction with other people, both strangers and acquaintances alike.

Behavioural competency 3

Competency Label: Challenging Ideas
Competency Description: This competency is about an individual facilitating or catalyzing change in an organization. Challenging Ideas emphasizes individual behaviors associated with questioning assumptions, challenging established views and arguing personal perspectives.

Behavioural competency 4

Competency Label: Checking Details
Competency Description: This competency is concerned with the careful checking and confirmation of details in a task. Another behaviour associated with the “Checking Details” competency is being accurate. Being accurate requires individuals to have a strong quality orientation as well as to be thorough and detailed in their approach when completing tasks in order to avoid making mistakes.

Behavioural competency 5

Competency Label: Examining Information
Competency Description: This competency serves to aid effective problem solving and requires being effective at probing and analyzing situations efficiently and accurately. This competency is important because without sufficient analysis, effective solutions become less probable. In addition, poor analysis makes it more likely that individuals become confused and anxious, bored, error prone or overwhelmed by detail, which also impacts negatively on successful problem solving.

Behavioural competency 6

Competency Label: Exploring Possibilities
Competency Description: Exploring possibilities is about individuals being effective at displaying behaviors associated with different situations or problems. Individuals are required to look at a problem and define it in an abstract manner. Unpacking a problem in terms of its underlying principles and basing the problem on sound theory typically allows for deeper insight into the true nature of the problem. This makes the nature of the problem more complete, more meaningful and therefore longer-term sustainable solutions more likely.

Behavioural competency 7

Competency Label: Interacting with People
Competency Description: This competency is about fostering relationships that benefit the organization as well as an individual effectiveness and efficiency. More specifically, it includes personal networking behaviors, making contact with others and strengthening relationships.

Behavioural competency 8

Competency Label: Interpreting Data
Competency Description: This competency is about interpreting data accurately with an emphasis on the processing and interpretation of numbers. This competency also includes the utilization of technology.

Behavioural competency 9

Competency Label: Meeting Timescales
Competency Description: This competency involves individuals adhering to time scales and meeting deadlines. The focus is therefore on being reliable at completing tasks and being punctual.

Behavioural competency 10

Competency Label: Producing Outputs
Competency Description: This competency is about ensuring that tasks are completed within the given timeframe. Behaviors that are emphasized in this competency include working at a fast pace, maintaining productivity and multi-tasking.

Behavioural competency 11

Competency Label: Providing Insights
Competency Description: This dimension is about providing insight with regards to aspects that are likely to have an impact on the organization. It is about making it clear to others what the implications of internal and external organizational environmental factors and processes are on the competitive position of the organization. Providing Insights should be done with a focus on improving the situation.

Behavioural competency 12

Competency Label: Team Working
Competency Description: This competency is about working well in a team. In order to develop this competency, individuals are encouraged to acknowledge the views and contributions of others, and to involve others indecision-making. 

Technical competency 1*

Competency Label: Data Analysis
Competency Description: Ability to analyses statistics and other data, interpret and evaluate results, and create reports and presentations for use by others.
Proficiency Level: SEASONED - Applies concepts without requiring supervision, able to provide technical guidance when required

Technical competency 2

Competency Label: Data Integrity
Competency Description: The ability to ensure the accuracy and consistency of data for the duration that the data is stored as well as preventing unintentional alterations or loss of data.
Proficiency Level: SEASONED - Applies concepts without requiring supervision, able to provide technical guidance when required

Technical competency 3

Competency Label: Database Administration
Competency Description: Refers to the knowledge and experience required to manage the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases.
Proficiency Level: SEASONED - Applies concepts without requiring supervision, able to provide technical guidance when required

Technical competency 4

Competency Label: Diagramming & Modeling
Competency Description: Measures proficiency in using the diagramming and modelling techniques vital for requirements
analyses.
Proficiency Level: SEASONED - Applies concepts without requiring supervision, able to provide technical guidance when required

Technical competency 5

Competency Label: Knowledge Classification
Competency Description: The ability to apply metadata to information to make it easy for other people to find.
Proficiency Level: SEASONED - Applies concepts without requiring supervision, able to provide technical guidance when required

Typical number of direct reports for this job

None

Typical number of indirect reports for this job

None

Financial accountability*

Type of Budget: No Budget Managed

Internal relationships*

Nature of Relationship: Provide a service to them
Description or examples: Provide Data science guidance, information services and ensure an effective data science capability, works closely with data analysts and data engineers to ensure an effective data management team. Collaborate with Technology and Project teams.

External relationships*

Role type of external contact: Vendors
Nature of relationship: Manage the relationship
Description or examples: Manage technical service delivery of technology vendors in the development, implementation, and customer service requirements for all data science requirements.

Work environment*

Physical Requirements: Open plan office                
Working conditions: • Africa region travel may be required
• Domestic / local travel may be required
Regulatory Requirements: None