Job Description
Role Purpose
The role holder will work closely with business stakeholders to understand their goals and determine how data can be
used to achieve those goals. The role holder will design data modeling processes, create algorithms and predictive
models in line with the business needs, and help analyze data and share insights with business stakeholders.
Main Responsibilities
1. Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive
business solutions.
2. Mine and analyse data from company databases to drive optimization and improvement of product development,
marketing techniques and business strategies.
3. Assess the effectiveness and accuracy of new data sources and data gathering techniques.
4. Develop custom data models and algorithms to apply to data sets.
5. Use predictive modelling to increase and optimize customer experiences, revenue generation, ad targeting and other
business outcomes.
6. Develop company A/B testing framework and test model quality.
7. Coordinate with different functional teams to implement models and monitor outcomes.
8. Develop processes and tools to monitor and analyse model performance and data accuracy.
9. Develop, implement and maintain databases.
10. Assess quality of data and remove or clean data.
11. Generate information and insights from data sets while identifying trends and patterns.
12. Prepare reports for executive and project teams.
13. Create visualisations of data.
Key Competencies
1. Problem-solving skills
2. Excellent written and verbal communication skills
3. Teamwork
4. Investigative skills
5. Interest in statistics
6. Interest in predicting trends and identifying patterns
7. Innovative thinking
8. Observation skills
9. Critical thinking
Qualifications
1. Degree in Mathematics, Statistics, Actuarial Science, Computer Science, Telecommunications, or any other related
field.
2. Relevant professional qualification.
3. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks,
etc.) and their real-world advantages/drawbacks.
4. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests
and proper usage, etc.) and experience with applications.
Relevant Experience
1. Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from
large data sets.
2. Experience working with and creating data architectures.
3. Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting,
Trees, text mining, social network analysis, etc.
4. Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
5. Experience creating and using advanced machine learning algorithms and statistics: Regression, Simulation,
Scenario Analysis, Modelling, Clustering, Decision Trees and Neural networks.
6. Coding knowledge and experience with several languages: C, C++, Java, JavaScript, etc.
7. Experience analysing data from third party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords,
Crimson Hexagon, and Facebook Insights
8. Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi and MySQL
9. Experience in visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3 and ggplot