01Responsibilities
1.
Drive
Data-Driven Insights to Optimize Marketing Strategies
Use Python or SAS to process and analyze large datasets for identifying trends, patterns, and insights that drive marketing strategy optimization.Employ SQL for querying structured data sources and ensuring data accuracy for analytical tasks.Leverage AI/ML techniques to develop predictive models, customer segmentation, and propensity scoring for more targeted marketing efforts.Use static analytics tools to create detailed statistical summaries and generate hypotheses for actionable marketing insights.2.
Analyze Large
Datasets to Track Key Performance Metrics
Perform advanced analytics using Python, SQL, or SAS to monitor campaign performance, customer acquisition, credit, and lending trends.Build and implement fraud detection models leveraging machine learning to ensure the integrity of marketing data and transactions.Provide actionable insights to business teams to enhance decision-making processes for customer engagement and business growth.3. Develop and Maintain Dashboards and ReportsUtilize tools like Power BI, Tableau, or custom Python-based dashboards for dynamic and interactive reporting of key performance indicators (KPIs).Design and automate reports using SQL queries to ensure timely delivery of metrics and insights.Provide comprehensive insights on metrics such as customer retention, cross-sell opportunities, and campaign ROI.4. Work with Clients and Stakeholders on Marketing Analytics RoadmapCollaborate with cross-functional teams, including product, sales, and marketing, to define a robust analytics roadmap.Present insights and recommendations effectively using data visualization techniques, ensuring stakeholders clearly understand the impact on credit, lending, and customer acquisition strategies.Incorporate Generative AI (Gen AI) models to create innovative solutions like personalized marketing campaigns, text-based customer sentiment analysis, and recommendation systems.
Additional Responsibilities with Skills:
Model Development and Validation:Build and validate machine learning models for fraud detection, customer credit scoring, and product recommendation using Python and AI/ML frameworks.Conduct feature engineering and hyperparameter tuning to enhance model performance.Fraud Detection and Risk Assessment:Develop and maintain predictive fraud models using machine learning algorithms to identify high-risk transactions and mitigate fraud in real time.Perform risk analytics to assess lending portfolios and ensure compliance with regulatory requirements.Customer and Retail Analytics:Leverage retail analytics to assess customer behavior, lifetime value, and purchase trends to personalize product offerings.Apply customer analytics techniques to understand the impact of marketing campaigns on customer engagement and satisfaction.Campaign Performance and Optimization:Analyze and optimize campaign performance by applying A/B testing, regression analysis, and advanced analytics models.Use AI/ML-based algorithms to predict customer preferences and improve marketing outreach strategies. .
