CRM Senior Data Strategist

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Overall Duties And Responsibilities
Data Analysis and Modeling

• Utilize advanced statistical techniques and machine learning algorithms to analyze large transaction datasets and extract meaningful patterns and insights. Develop predictive models to forecast future trends, identify opportunities, and mitigate risks.
• Perform exploratory data analysis to uncover hidden insights and opportunities for optimization of but not limited to direct marketing campaigns, customer value management, and channel management.
• Collaborate with data engineering teams to optimize data pipelines and maintain high data quality standards.
• Implement data validation techniques to identify and rectify any anomalies or inconsistencies in the data.

Insight Generation

• Translate complex data findings into clear and actionable insights that contribute to business objectives.
• Collaborate with cross-functional teams to understand business requirements and prioritize analytics initiatives accordingly.
• Develop data-driven narratives and visualizations to communicate insights effectively to non-technical stakeholders.

Strategy Development

• Work closely with business leaders to identify key challenges and opportunities that can be addressed through data-driven strategies.
• Provide strategic guidance and recommendations based on data analysis to support decision-making processes.
• Continuously monitor and evaluate the effectiveness of implemented strategies and adjust as needed based on new insights.

Displays

• Customer-centric, value-based mindset
• Deep understanding of precision marketing frameworks and principles of marketing science
• Strong intuition about statistical modeling and machine-learning techniques
• Proficiency in data analysis and statistical reasoning
• Strong communication skills and strong stakeholder management skills

Delivers

• Insighting Reports & Analysis, Deep-dive Studies, Exploratory Data Analysis that mines meaningful trends & patterns in data
• Predictive models to forecast future trends, identify opportunities, and mitigate risks
• Other data products that supports CRM objectives

KEY RESULT AREAS AND PERFORMANCE INDICATORS

Customer

• EMAU / MAU contribution
• Use Case Diversity contribution
• Customer Engagement Score

Financial

• Gross Margin (in Revenue)
• Customer Profitability
• Customer Lifetime Value

Process

• Meeting SLA and TAT commitment to stakeholders
• Identifying areas in the process for automation
• Contribution and improvement in process pipeline

People

• Customer satisfaction score (proponents / stakeholder)
• Engagement and participation in team-building events
• Collaborative dynamic with co-workers

NATURE OF PROBLEMS ENCOUNTERED

• Lifts observed in experiment are not observed in production
• Investigate most likely points of failure: data collection, analyses, implementation, external factors
• Develop recommend courses of action and present to business

Sample Projects

• Recommender System development
• Enhanced targeting for educational campaigns
• Enhanced targeting for incentive programs
• Optimal customer journey prediction for lifts in revenue / frequency / activation
• Automation of processes and pipelines (e.g. audience creation for campaign/experiment blasts, post-campaign/experiment evaluation)

The Role Will Be Involved In The End-to-end Process

• from scoping the project, transforming the problem into a data science question, proposing a solution, developing the models and pipelines, implementing the experiment, evaluating the performance in production (real life), improving the approach / redefining the problem, and maintaining the models in production.

RESOURCES MANAGED

Assets / Resources

• Customer transaction data
• Customer demographic data

Nature of Responsibility / Accountability
Handling private and sensitive customer data for deep-dive analysis, insighting, modeling, campaign audience-targeting, etc.

Qualifications
Minimum requirements in terms of experience and background, skills and competencies for sourcing purposes.

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, or a related field.
• Proven experience in data analysis, statistical modeling, and machine learning techniques.
• Proficiency in programming languages such as Python, R, or SQL.
• Strong communication and storytelling abilities with the capability to simplify complex concepts for diverse audiences.
• Experience working with business stakeholders to understand requirements and deliver insights that drive business value.
• Excellent problem-solving skills and a passion for uncovering actionable insights from data.

INTERDEPENDENCIES, INTERNAL ENGAGEMENT, & INTERACTIONS

Immediate Superior/s

• CRM Analytics Head (Benj Danao)

Peers

• Data Analysts
• Data Scientists
• Data Engineers
• ML Engineers
• Product Managers
• CRM Business Partners

Direct Reports

• N/A

Others (within Mynt)

• Growth & Product Teams

Others (external Mynt)

• CRM Solutions clients

What We Offer
Opportunity for career growth and development in the #1 FinTech company in the country Working with a dynamic and highly collaborative team who want to change the game A company that values their people with highly competitive and flexible compensation and benefits package