Senior Data Scientist

SUMMARY:
Unlock Your Potential with Gijima Staffing Solutions!
We are recruiting on behalf of our client, a retail industry giant

Are you passionate about leveraging data to drive business strategies and uncover opportunities? Do you thrive in dynamic environments where your analytical skills can make a significant impact? If so, we have the perfect opportunity for you!

POSITION INFO:

Main Purpose:

What You Bring:

Qualifications and Experience:

• Educational Background: University degree in Business, Mathematics, Statistics, Operational Research, Economics, Engineering, Risk Management, or equivalent industry training and experience.
• Strategic Experience: Proven strategy development and implementation experience using ADA analytics.
• Outsourcing Experience: Experience working in an outsourced environment and effectively implementing solutions using both internal and external resources.
• Technical Proficiency: Experience in SAS Advanced programming (or equivalent) for statistical analysis and manipulation of very large data sets.
• Analytical Experience: At least 3-5 years of analytical experience on a consumer portfolio dataset (retail, banking, telecoms, etc.). Retail experience advantageous.
• Industry Knowledge: Deep knowledge of the Retail Industry and its operating dynamics.
• ADA Techniques: Knowledge and application of ADA techniques across the consumer life cycle.
• Analytical and Business Acumen: Proven ability in analytics, business acumen, and presenting/communicating recommendations across all organization levels.
• Team Collaboration: Ability to work with diverse teams to ensure effective implementation of projects.
• Financial Metrics Understanding: Understanding of financial metrics in relation to ADA management and associated forecasting.
• Software Proficiency: High proficiency in SAS (intermediate or advanced level) or equivalent structured language analytical software. Proficiency in Microsoft Office (Excel, Word, PowerPoint, etc.), ideally using macros.
• Data Integrity: Ability to cross-check data integrity after an initial validation exercise.
• Portfolio Understanding: Comprehensive understanding of portfolio fields and data relationships to improve analytical recommendations.

Key Responsibilities:

Evolve the Retail ADA Strategy:

• Strategy Development: Evaluate the performance of ADA strategies to answer key business questions or discover opportunities for improvement, increased revenue, or reduced costs.
• Documentation and Recommendations: Document, recommend, and present key ADA results/findings. Design improved strategies that outperform previous ones, following appropriate governance processes for strategy approval.

Forecasting and Regular ADA MI:

• Forecasting: Forecast key ADA metrics as per operating plans.
• MI Reports: Produce timely and accurate MI reports to track ADA performance and conduct variance analysis explaining actual values against business forecasts.
• Recommendations: Make appropriate recommendations to address unfavorable variances.

Lead Analytical Projects:

• Project Development: Develop and drive project plans for major ADA development, securing resources to ensure on-time, within-budget, and to-quality implementation.
• Effectiveness and Efficiency: Prove the business effectiveness and/or efficiency of analytically driven implementations through Post Implementation Reviews. Recommend and implement appropriate champion/challenger strategies to continually “test and learn” to improve ADA profitability.

Quality and Governance Standards:

• Performance Standards: Oversee and ensure that ADA analytics are performed to the required standards.
• Governance Policies: Ensure awareness, adherence, and compliance to the Governance Policies and any other related policies and procedures.

Stakeholder and Resource Management:

• Alignment: Align ADA implementations to WW Business Unit resourcing requirements to ensure they’re on-time, within budget, and to the required standards.
• Integration: Effectively integrate with Woolworths’s stakeholders to ensure implementation practicality and business quality as appropriate.
• Coaching and Mentoring: Coach and mentor Data Scientists, Analysts, and others in the team as required.