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Shukla Avula - Lead Data Scientist

[email protected]

Location: Houston, Texas, USA

Relocation: Local to Texas

Visa: H1B

Shukla Avula +1 832-225-6700 [email protected] EXPERIENCE SUMMARY Data Scientist with 8+ years of experience delivering actionable insights and driving operational efficiency through advanced analytics, machine learning solutions, and data-driven decision-making. Proven expertise in extracting, analyzing, and interpreting complex big data to solve critical business challenges and optimize processes. Skilled in training, validating, and deploying scalable machine learning models using Python, R, and other advanced tools to deliver impactful solutions. Adept at collaborating with cross-functional teams to tackle complex problems, meet tight deadlines, and execute projects in fast-paced environments. Recognized for strong leadership abilities, a proactive mindset, and exceptional problem-solving skills to address business challenges effectively. Experienced in leveraging data visualization tools such as Tableau and Power BI to translate complex findings into clear insights for stakeholders, enabling informed decision-making. Passionate about applying statistical techniques, A/B testing frameworks, and experimentation methodologies to improve customer engagement, mitigate risks, and enhance operational performance. A positive thinker and self-starter with excellent written and verbal communication skills who thrives both independently and in team-oriented environments. SKILLS: Tools and Languages: Python, PySpark, R, SAS (SAS EG, Miner, Visual Analytics), SQL, VBA, Tableau, Power BI, Snowflake, Microsoft Azure, Google Analytics, MySQL, Oracle, Teradata Key Competencies: Machine Learning (Random Forests, Gradient Boosting, Decision Trees, PCA), Predictive & Prescriptive Analytics, A/B Testing, Customer Segmentation, Fraud Detection Algorithms, Statistical Techniques (T-test, ANOVA, Regression Analysis), Data Visualization EDUCATION: Oklahoma State University Aug 2015 - May 2017 Master s in Industrial Engineering & Management Vasavi College of Engineering, India Aug 2010 - May 2014 Bachelor s in Mechanical Engineering PROFESSIONAL EXPERIENCE: Client: Discover Financial Services - Houston, TX Oct 2018 Current Role: Lead Data Scientist Responsibilities: Achieved over 85% improvement in operational efficiency by identifying optimization areas, evaluating opportunities, and automating processes using Python. Managed all process improvements and enhancements within the area of responsibility, including evaluation of efficiency and effectiveness, cost-benefit analysis, process mapping, workflow analysis, and process re-engineering. Led the implementation of advanced analytics strategies, including customer segmentation and prescriptive analytics, to provide actionable recommendations for addressing business challenges. Conducted exploratory data analysis on extensive datasets to identify patterns and trends, resulting in enhancements to existing models and workflows. Applied advanced machine learning techniques (e.g., Random Forests, Gradient Boosting) to build predictive models that forecast customer behavior with high accuracy, optimizing decision-making processes. Leveraged popular machine learning libraries such as Scikit-learn, TensorFlow, Pandas, NumPy, and Statsmodels for model development while applying statistical techniques (e.g., hypothesis testing) to validate results. Conducted A/B testing frameworks to optimize marketing strategies and improve operational performance. Utilized a combination of rule-based techniques and machine learning to effectively identify irregularities or deviations, enabling the accurate detection of fraudulent transactions while minimizing false positives. Introduced new measures to prevent fraud, including real-time transaction monitoring, to detect and prevent potentially fraudulent activity, reducing the risk of financial loss to the company. Established data-driven rules and thresholds for flagging suspicious activities and created automated alerts that enabled faster responses to potential fraud cases. Served as a Product Owner for multiple data science initiatives, managing project backlogs in Jira, defining user stories, prioritizing tasks based on business needs, and ensuring timely delivery of high-impact solutions. Facilitated Agile ceremonies such as sprint planning, daily stand-ups, retrospectives, and sprint reviews to drive collaboration between teams following the Scrum framework. Conducted end-to-end testing of machine learning models and automated workflows to ensure accuracy, reliability, and alignment with business requirements before deployment into production environments. Collaborated with cross-functional teams across engineering, finance, and product management in an Agile environment to streamline processes, align project goals, and ensure seamless model deployment pipelines using tools like Git for version control. Played a key role in data governance initiatives, ensuring data quality, consistency, compliance with regulatory standards, and proper documentation across all data science projects. Collaborated with various business partners to analyze the effects of enterprise network initiatives on recovery processes, integrating necessary process changes into recovery strategies and providing informed recommendations. Developed scalable data visualization solutions using Tableau and Power BI to effectively communicate insights derived from exploratory data analysis (EDA), predictive modeling, and experimentation results to stakeholders. Developed and upheld multiple data visualization solutions to proficiently convey insights and crucial findings to stakeholders. Leveraged statistical testing frameworks to evaluate the impact of new initiatives on customer engagement and operational performance, driving data-driven decision-making across departments. Client: Barry University | Miami, FL June 2018 - Oct 2018 Role: Data Scientist Responsibilities: Performed data cleaning processes that involved identifying potential issues with study data and collaborating with the research team to address encountered issues and establish procedures ensuring data quality. Created predictive models for forecasting enrollment and utilized various visualizations across different complex and large-scale datasets, presenting the findings in an easy-to-understand format for research purposes. Developed interactive dashboards using Tableau to track metrics related to incoming students, including enrollment trends, demographic breakdowns, and retention rates, enabling the institution to monitor progress and make informed decisions. Collaborated with the institutional research department to identify pain points in the student onboarding process by analyzing behavioral data, leading to website changes that improved user experience and increased application submissions. Designed and executed a direct mail marketing campaign targeting prospective students based on segmentation analysis, resulting in a 15% increase in engagement rates and higher enrollment conversions. Built machine learning models to predict student enrollment likelihood based on historical data, enabling targeted outreach efforts and improving overall recruitment efficiency. Provided institutional data for external constituents such as IPEDS (Integrated Postsecondary Education Data System) and ICUF (Independent Colleges & Universities of Florida), ensuring compliance with reporting requirements on metrics like enrollment, retention, and admissions. Supported institutional effectiveness efforts by working with academic programs, administrative units, and support services to align with accreditation criteria set by SACSCOC (Southern Association of Colleges and Schools Commission on Colleges). Client: LexisNexis | Oklahoma City, OK July 2017 - Mar 2018 Role: Data Analyst Responsibilities: Performed data mining, extracted and explored data in large and different types of databases, analyzed, tested complex datasets and provided insights and conclusions Supported the Business groups by using different customer segmentation, clustering, data sampling techniques, user behavior analytics and Performed regression analysis to check the strength of correlations between customer variables. Collaborated with marketing teams to make fact-based decisions on marketing programs to increase profitability, retention, and engagement. Worked closely with marketing clients to develop strategies for improving customer retention by analyzing user behavior patterns and identifying at-risk customers for targeted outreach campaigns. Addressed data quality issues by cleaning and preprocessing raw data imported from manual sources, ensuring accuracy and consistency for downstream analysis. Built automated reports and interactive dashboards using Tableau and Power BI to track key performance indicators (KPIs) such as customer churn rates, campaign effectiveness, and revenue growth trends. Client: Oklahoma State University | Oklahoma City, OK Aug 2015 - May 2017 Role: Marketing Analytics Intern Responsibilities: Improved the performance by utilizing analytical platforms such as Google Analytics to gather and interpret the data to improve digital marketing strategy and user experience on websites by analyzing website traffic. Conceptualized and designed highly intuitive data visualizations that are adaptable for different datasets. Analyzed web traffic data to measure different web analytics metrics such as hit, bounce, and exit rates. Conducted in-depth analysis of Medicare and Medicaid claims data as part of a project with CHSI, identifying key relationships between patient demographics, healthcare utilization, and risk factors. Built predictive models to determine high-risk patient groups based on historical claims data, enabling targeted interventions to improve health outcomes. Explored large-scale health datasets to uncover trends in healthcare costs and service usage, providing actionable insights for optimizing resource allocation in healthcare programs. Client: Alliance Prosys | India July 2014 - June 2015 Role: Data Analyst Responsibilities: Conducted data analysis with SQL and SAS, involving data extraction, cleansing, and preprocessing, while also utilizing PROC SQL for retrieving and reporting on SAS data sets. Performed quantitative analysis to gain insights into customer behavior and demographics, generating data tables and figures as per the statistical analysis plan using procedures like PROC REPORT, PROC FREQ, PROC TABULATE, PROC MEANS, and PROC SUMMARY. Keywords: artificial intelligence business intelligence rlang Florida Texas