In terms of technical proficiency, I have demonstrated exceptional skills in programming languages and tools such as Python, R, SAS, SQL, and PySpark. My ability to manipulate and analyze data is augmented by libraries like Pandas, SciPy, SciKit-Learn, TensorFlow, and NumPy, allowing for advanced computational tasks. I am also adept with deep learning frameworks like PyTorch. My experience with version control is evidenced by my practical experience in GitHub.

My expertise in data science is further underscored by my familiarity with a broad spectrum of supervised learning techniques, including Neural Networks, Random Forest, Naïve Bayes Classifier, KNN, XGBoost, and various regression models. My track record in A/B testing is commendable, and I possess an in-depth understanding of unsupervised learning methods such as K-Means and Hierarchical Clustering.

On the analytics and visualization spectrum, my capabilities are demonstrated through certifications and extensive usage of tools like Tableau, Power BI, Looker, Adobe Analytics, and Google Analytics. I am proficient in drawing data-driven insights and making strategic decisions, further enhanced by my adeptness in leveraging Decision/Strategy Platforms.

I have honed expertise in utilizing AWS services for data management and analytics. This includes hands-on experience with Amazon SageMaker, AWS Glue, and AWS Data Pipeline for streamlined data processing and model development. I regularly interact with storage solutions like Amazon S3 and have gained familiarity with AWS Lake Formation, which solidifies my capabilities in handling large-scale data infrastructures.

In summary, my broad educational background, combined with my vast skill set in data sciences, analytics, programming, advanced analytics tools, and cloud solutions, not to mention my leadership acumen, earmarks me as an invaluable asset for any organization aiming to harness data for a competitive advantage.

Leading with empathy and foresight, I have cultivated a team environment that values innovation, collaboration, and results-driven strategies. This approach has enabled my team to flourish, unlocking their potential to achieve remarkable feats.

In my role, I championed profitable growth strategies for credit and retail card portfolios by harnessing the power of innovative financial decision management. My leadership was instrumental in pioneering advancements across the credit lifecycle, from credit underwriting and fraud prevention to collections and financial modeling. A testament to my hands-on leadership style, I personally spearheaded several data science initiatives, always staying at the forefront of technological and analytical advancements.

My team-centric approach, combined with a strategic vision, facilitated the deployment of cutting-edge AI/ML models. These models were pivotal in driving strategy changes, leading to amplified portfolio growth and enhanced customer experiences. Under my guidance, we achieved significant operational efficiencies, evident from a marked reduction in collection operations headcount and consecutive years of impressive recovery growth. My ability to integrate AI/ML solutions into our operations revolutionized account authorizations for the ACH-based card program, delivering an incremental 500K accounts annually and boosting store sales by a staggering $4B/year.

Further showcasing my commitment to operational excellence, I led the innovation of an ACH/check fraud detection system, resulting in substantial savings of approximately $2M/year. This keen sense of innovation also extended to our marketing strategies, where we leveraged AI/ML to generate an impressive $2.5M in additional sales for individual store openings.

Under my guidance, the team excelled in fostering cross-functional collaborations, bringing innovative data science and advanced analytics solutions to the forefront. Together, we took on transformative projects, significantly enhancing the customer journey, streamlining operations, and boosting revenue growth.

Our team's unique culture was characterized by innovation, collaboration, inclusiveness, and decisive action. This wasn't just a philosophy; it was evident in our daily interactions and the impactful projects we executed. We set the standard for enterprise-wide strategies in insights generation, data visualization, and report migration.

Among our proudest achievements were the development, validation, and deployment of several AI/ML API models. These models, central to modern data analytics, were pivotal in our strategy. I took an active role not just in leadership but also as the principal developer for many key data science initiatives. This hands-on approach ensured our team consistently aligned with the organization's goals while remaining at the forefront of technological innovation.

Empathy has been more than a guiding principle; it has been an operational philosophy within my leadership framework. By recognizing and valuing each team member's individual journey, I have cultivated a work environment where every voice is heard, and every contribution is integral to our collective progress. This empathetic engagement has been critical in empowering teams ready to embrace future challenges with creativity and insight.

My leadership has been characterized by direct involvement, establishing a culture of shared commitment and interdependence. Working closely with my teams, I have set a standard of dedication, becoming both a mentor and a collaborator in the intricate processes of innovation. My hands-on approach has been central to steering data strategy and analytics initiatives, underscoring my dedication to overseeing and propelling our shared achievements actively.

Trust has been the foundation of empowerment within my teams. By entrusting team members to lead and innovate, I have cultivated a sense of ownership and pride in their work, always reinforced by supportive mentorship. This balance has encouraged a culture where autonomy is harmonized with the necessary support to master the nuances of strategic and innovative undertakings.

Celebrating team successes has been a source of satisfaction in my leadership journey. Highlighting individual and team accomplishments has not only encouraged confidence but has also been instrumental in advancing professional development, fostering an environment where growth and recognition are deeply intertwined.

The diversity of thought, experience, and methodology has been a cornerstone of my approach to leadership. I have actively fostered inclusive teams, recognizing that everyone’s perspectives are crucial for innovation and superior business results. My leadership has embraced this diversity, ensuring that our solutions are as multifaceted and inclusive as the society we serve.

Reflecting on the rich talent and determination I have been tasked to guide, the development of my teams has been a definitive marker of my leadership philosophy. I have nurtured an environment where the analytical thinker delving into predictive analytics and the strategic planner elevating business intelligence coalesce, defining a legacy of continuous improvement, ingenuity, and an unwavering commitment to excellence.

In an impressively brief three-month timeframe, my role as a seasoned data scientist led a diverse team, including a proficient software engineer, a skilled database engineer, and myself, to create a sophisticated transaction alert system. Our mission was clear: to identify and deter abusive behavior within the welfare program.

The project's technical prerequisites were demanding. We needed to process over a million individual transactions daily in batch mode, condense our findings into comprehensive summaries at the welfare recipient level, and promptly alert fraud detection agents when potential benefit fraud was detected. An additional challenge was the mandate to develop and deploy the entire system using SAS and SAS modules, adding complexity to our task.

As both the project manager and a seasoned data scientist, I played a pivotal role in overseeing the entire initiative and collaborating closely with our team to conceptualize and design the system. Moreover, my core contribution lay in developing the AI model that would serve as the project's cornerstone.

Leveraging the power of SAS, I meticulously crafted a Neural Network model. This model harnessed historical data on welfare recipient behavior, distinguishing between fraudulent and non-fraudulent activities. To enrich our dataset for modeling, we incorporated comprehensive transaction details, including the types and quantities of food purchased, given the government's stringent requirements.

After rigorous data pre-processing and the creation of feature detectors, I successfully crafted a high-performing Neural Network model. This model possessed the remarkable ability to accurately predict the likelihood of an individual transaction being fraudulent. However, our ultimate aim was to detect welfare benefit recipients engaged in fraudulent activities, and a single suspicious transaction was insufficient to trigger an investigation. Consequently, we had to aggregate individual transactions to the recipient level, generating an overarching index that would earmark recipients for further scrutiny by our investigators.

With the data formatted by our database engineer, the software engineer meticulously crafted a decision system. This platform enabled our investigators to review and delve into the activities of welfare benefit recipients, document their investigative findings, and, when necessary, terminate benefits. Notably, this invaluable information was used to recalibrate our model each evening, ensuring it remained finely tuned and adaptive to emerging patterns.

The fruits of our labor were nothing short of transformative. The project resulted in a significant reduction in welfare fraud, underscoring our commitment to preserving the integrity of the welfare program. Furthermore, the efficiency of welfare fraud investigators soared, as they could now focus their efforts with laser precision.

This endeavor stands as a testament to my leadership, technical prowess, and decision science capabilities. Together with my dedicated team, we championed data-driven solutions that safeguarded the welfare system and maximized its impact on those truly in need.

Project Scope and Objectives:

The project's scope encompassed the development of an internal matching AI algorithm capable of accurately monitoring new customer acquisitions against prior write-offs.

The key objectives included:

1. Precise Detection: Create a robust algorithm to identify customers with prior write-offs accurately.

2. Decision Rule Base: Establish a comprehensive set of decision rules and overrides for handling inconsistencies between new customer acquisitions and prior customers with write-offs.

3. Logging: Implement a logging mechanism to maintain a record of system activities and decision-making processes for auditing purposes.

4. Efficiency: Optimize the solution to ensure minimal impact on application processing times for new services.

Technical Approach:

To meet these objectives, I adopted an advanced technical approach, combining various methodologies and techniques using Python, PySpark, and AWS SageMaker:

1. Locality-Sensitive Hashing (LSH): I leveraged LSH, a set of techniques known for significantly speeding up the search for neighbors or near-duplicates in large datasets. This allowed for efficient matching of incoming applications with prior write-off accounts.

2. Jaccard Similarity Coefficient: The Jaccard similarity coefficient was employed to fine-tune the similarities within the LSH nearest neighbors, enabling a more precise comparison.

3. Fuzzy Matching: Given the existence of key elements in the comparison between incoming records and existing write-off records, fuzzy matching techniques were incorporated to derive an overall similarity calculation, further enhancing accuracy.

4. Optimization: Optimization techniques were applied to ensure that the comparison process did not impact the time required to process an application for a new service.

Implementation and Deployment:

The developed model was deployed in AWS SageMaker using Python, and it was seamlessly integrated into the AWS infrastructure as an endpoint for the customer acquisition engine. Notably, the model demonstrated remarkable efficiency, with processing times of less than 2 milliseconds.

Outcomes and Impact:

1. Risk Mitigation: The accurate detection of customers with prior write-offs resulted in a significant reduction in risk exposure.

2. Revenue Growth: By preventing revenue losses associated with the previous system's inaccuracies, the company experienced exponential revenue growth.

3. Enhanced Customer Experience: Good customers were no longer turned away, leading to improved customer satisfaction and loyalty.

In conclusion, this project showcased my leadership, technical prowess, and system design capabilities in tackling a critical business challenge. Leveraging my decision science expertise, I developed a model that not only rectified a pressing issue but also propelled the company towards greater success and financial stability.

The project required me to thoroughly analyze the existing customer acquisition process, gather and analyze analytical data related to customer behavior, and evaluate the performance of existing AI models used in the process.

The core mission of this endeavor was to architect a system with a razor-sharp focus on customer acquisition. It required us to navigate the intricate landscape of credit risk, churn prediction, and the regional disparities in satellite capacity. The overarching aim was to optimize the Average Revenue Per User (ARPU), a vital metric in our industry.

Primary Technologies:

Pyton, AWS Cloud Formation, AWS Step Functions, AWS DataLake, AWS integration with SageMaker Endpoints, AWS CDKs.

Key Achievements:

1. In-Depth Code Review:** I undertook a meticulous examination of the Python codebase, diving deep to gain a profound understanding of the existing system's intricacies. This foundational effort laid the groundwork for what followed.

2. Efficient Legacy Code Utilization: Recognizing the inherent value of the legacy code, I strategically incorporated it into our redesign. This astute decision not only saved precious time but also conserved valuable resources.

3. Strategic Talent Acquisition: To meet the organization’s aggressive growth targets, I meticulously handpicked a team consisting of 3 engineers and 1 data scientist. This team played a pivotal role in the success of our project.

4. Exceeding Expectations on a Tight Timeline: Despite the formidable challenge of an impending satellite launch, we not only met but surpassed our project goals ahead of schedule.

Outcome:

The results speak for themselves. The organization now boasts an advanced Customer Acquisition System that not only met but exceeded growth expectations. This system expertly manages credit risk, churn prediction, and regional satellite capacity, resulting in a substantial increase in ARPU.

Demonstrated Skills:

1. Python Expertise: My deep dive into the codebase showcased my mastery of Python, a crucial asset in this endeavor.

2. System Design Proficiency: My skills in engineering system design were pivotal in crafting a robust and efficient acquisition engine.

3. Effective Team Leadership: I led a high-performing cross-functional team, fostering collaboration and synergy among team members.

4. Executive-Level Communication: My ability to communicate effectively with executive leadership secured essential support and resources for the project.

5. Cost-Efficient Planning: I meticulously managed project costs, ensuring we stayed within budget while delivering exceptional results.

This project stands as a testament to my ability to deliver transformative outcomes through strategic leadership, technical expertise, and a relentless pursuit of excellence. It showcases my commitment to achieving remarkable results, even in high-pressure scenarios.

Client's Objective:

The core mandate from the client, a prominent grain additive manufacturer, revolved around a dual-pronged objective: to assess the efficacy of their product in reducing mortality rates among poultry and to enhance the growth metrics, specifically targeting an increase in breast size among chickens. The end goal was not merely incremental improvement but to revolutionize their product’s impact in tangible, revenue-augmenting ways.

Project Goals and Data Science Application:

My primary mission was to dissect an extensive array of the client's data, employing an arsenal of data science methodologies to unearth deep insights and develop predictive solutions. This was not a trivial pursuit of data analytics but a quest to construct a robust predictive model using Artificial Intelligence, particularly Neural Networks, known for their ability to model complex, non-linear relationships within vast datasets.

To surpass traditional analysis, I expanded the data ecosystem by integrating external datasets that could unveil correlations previously untapped. This explorative process encompassed the addition of environmental factors such as weather patterns, ground temperature, and a myriad of other potential influencers to the analytical matrix.

Technical Approach:

The technical journey involved the deployment of a sophisticated proprietary platform termed the Data Mining Workstation. Within this environment, I orchestrated the development of multiple advanced Neural Network architectures, meticulously fine-tuning hyperparameters to ensure the precision of the predictive models. Additionally, I leveraged complex partial derivative algorithms to deconstruct the AI model's decisions, thereby isolating and quantifying the impact of each variable within the network.

Outcomes:

The project outcomes exceeded all expectations. Not only did the analysis confirm the effectiveness of the client's grain additives, but it also unveiled a suite of additional improvements. The Neural Network models recommended a portfolio of alternative additives and stipulated optimal environmental conditions under which they could be administered for maximum efficacy.

The recommendations derived from the AI models were met with resounding approval from the client, marking a significant milestone in their operational strategy. The analytical deliverables were not just solutions; they were a paradigm shift in how the client perceived the role of data-driven decision-making in agricultural sciences.

The triumph of this project was multi-faceted: It underscored the transformative potential of AI in the agribusiness sector, it demonstrated my ability to navigate and synthesize complex data terrains, and most importantly, it paved the way for enhanced poultry health and growth, with implications for the broader field of agricultural production.

Objective:

To boost incremental account acquisitions and store sales by leveraging AI/ML models to refine the authorization process for an ACH-based card program without necessitating credit checks.

Background:

A new financial services card was launched with the dual aims of escalating in-store sales and engaging customers with a rewards program without obliging them to apply for a new credit card. This card operated on an ACH framework, charging the user’s checking account on the day of purchase. However, the requirement of a hard credit inquiry for card authorization caused confusion and dissatisfaction among customers, as they were not applying for a traditional credit card. This led to a decline in adoption rates and customer satisfaction.

Challenge:

The main challenge was to eliminate the need for a credit inquiry while still accurately assessing the risk of offering the ACH card to customers. This necessitated the identification and integration of alternative data points that could predict credit risk reliably.

Research and Data Acquisition:

Extensive research was undertaken to identify non-credit bureau data sources that could serve as indicators of creditworthiness. Various data types were examined for predictive capabilities, including transactional, behavioral, and demographic data. Intuitive data points were discovered through meticulous exploration, which showed potential in replicating the risk assessment functions of a credit score.

Data Engineering:

The project involved complex data engineering tasks to prepare the newly identified datasets for analysis. This included data cleaning, transformation, and the creation of derived variables that could be indicative of credit risk. A framework was established to score credit risk in real-time during the card application process, thus enabling immediate decision-making without the delay of a traditional credit check.

Model Development:

A robust Neural Network model was designed using SAS, chosen for its advanced analytics capabilities. The model integrated the engineered non-traditional data variables to effectively predict the likelihood of default without requiring a credit check. Multiple architectures were tested to optimize for both precision and recall, ensuring that the model minimized risk while maximizing the approval rate for potential cardholders.

Model Validation and Testing:

Rigorous validation techniques, including k-fold cross-validation and testing on unseen data, were employed to ensure the model's generalizability. The performance metrics confirmed that the model was significantly effective at identifying credit risk using the engineered data.

Deployment Strategy:

The deployment strategy was engineered to integrate seamlessly with the existing card application process. This included the development of APIs that allowed real-time data ingestion and risk scoring and the implementation of monitoring tools to track the model’s performance and trigger retraining when necessary.

Impact:

The redesigned authorization process eliminated the need for hard credit inquiries, simplifying the customer experience and increasing card adoption rates. The AI/ML model contributed to acquiring 500K additional accounts per year and a notable $4 billion increase in annual store sales. Moreover, eliminating credit checks for the ACH card program led to restoring customer satisfaction and confidence in the financial product.

Conclusion:

The ACH card program’s revitalization through AI/ML modeling is evidence to the power of innovative data usage and machine learning in transforming financial services. By intelligently navigating traditional credit risk assessment methods, the project met its objectives and set a new precedent for customer-centric financial product offerings.

Approach:

A comprehensive analytic approach was undertaken, involving:

1. Data Analysis and Preparation:

Intensive data analysis using R, SAS, and SQL to sift through merchant, customer, and sales data.

2. Merchant Segmentation:

Implementation of unsupervised learning models via advanced R programming to classify health care providers according to engagement levels and other relevant factors.

3. Visualization and Reporting:

Deployment of Tableau for presenting segmentation outcomes, organizing providers by health service type and engagement level.

Data Engineering:

The project involved complex data engineering tasks to prepare the newly identified datasets for analysis. This included data cleaning, transformation, and the creation of derived variables that could be indicative of credit risk. A framework was established to score credit risk in real-time during the card application process, thus enabling immediate decision-making without the delay of a traditional credit check.

4. Engagement Optimization:

Development of Neural Network models in R to forecast the likelihood of a representative’s visit increasing a provider's engagement, identifying merchants with the greatest potential for growth.

5. Time Series Analysis:

Application of R programming for time series analysis to determine optimal visit schedules for representatives to health care providers.

Outcomes:

The project yielded significant enhancements across the board.

Transitioned from an arbitrary visitation plan to a data-driven schedule, focusing on providers with high potential for engagement growth.

Boosted the number of actively participating providers in the health care card program.

Elevated the sales representatives' efficiency, leading to better educational efforts and increased earnings.

Increased revenue for the company through strategic merchant engagement.

Customers benefitted from greater healthcare access with the ability to manage costs effectively using the card.

Conclusion:

By leveraging advanced analytics, the project not only improved the engagement and usage of the health care card but also significantly enhanced the effectiveness and productivity of the sales representatives. This holistic improvement created a win-win situation, strengthening the financial health care program's market position and ensuring benefits for all stakeholders involved.

Development and Analytical Approach:

The project employed Python as the backbone for data analysis and summarization. Python's extensive libraries and frameworks facilitated comprehensive data processing, from cleaning and transformation to advanced analytical computations. This foundational analytical work paved the way for meaningful insights, which were then visually articulated through Tableau.

The resultant Tableau dashboards were precisely crafted to serve a dual purpose:

1. To illuminate current customer acquisition volumes through intuitive volume metrics.

2. To provide comparative metrics that allowed stakeholders to perceive how these volumes evolved over time and to gauge the implications of these trends.

Customization and Segmentation:

One of the pivotal aspects of the dashboard design was the high degree of customization and segmentation. It was imperative to enable a multi-faceted review of all metrics by various business dimensions, including:

• Market Segment
• Sales Strategy
• Pricing Strategy
• Credit Classification
• Distribution Channel
• Satellite Utilization
• Satellite Beam Capacity

This segmentation ensured that the visualizations were not just general overviews, but detailed analytical tools that provided granular insights into each aspect of the customer acquisition process.

Trend Analysis and Predictive Insights:

At the heart of the visualization dashboards was an advanced trend analysis feature. This component was instrumental in identifying and interpreting trends to assess their impact on key performance indicators (KPIs). Moreover, leveraging these trends, the system was equipped to produce predictive forecasts, offering a forward-looking view into potential future states of the KPIs.

Dynamic Interaction and Executive Accessibility:

An integral requirement for the dashboards was dynamic interactivity. Users needed to delve deeper into specific areas of interest or concern, demanding a drill-down capability within the dashboards. Such functionality ensured that the tool was not just a static report but an interactive analytical platform.

Furthermore, it was paramount that the dashboards were accessible and comprehensible to all business units, with particular emphasis on Executive Leadership. The visualizations were designed with a clear, intuitive interface, facilitating quick comprehension and enabling leaders to derive insights without the necessity for deep technical knowledge of the underlying data or analytical methods.

Project Outcome and Business Impact:

Post-deployment, the dashboards achieved broad adoption across the company. They emerged as a cornerstone tool for accessing and interpreting business performance metrics. The trend insights and predictive capabilities of the system allowed for a more nuanced understanding of the customer acquisition process and its effects on the organization's KPIs.

In conclusion, the project not only met its intended objectives but also established a new benchmark for data-driven decision-making within the company. By providing a rich, interactive, and intuitive platform for trend analysis and forecasting, the project significantly enhanced the company's ability to respond to dynamic market conditions and to strategize effectively for future growth.