SUMMARY

• Technology driven and detail oriented data scientist with over nine years of combined industry experience in big data management , data manipulation scripts , data collection and cleaning , data processing , descriptive analytics, business analysis, revenue forecasting and predictive analytics etc.
• Expertise in the use of data manipulation scripts like Python, R, and SQL. Proven skills in machine learning techniques and deep neural networks. Competent communicator and confident presenter to report analytical findings to members of the leadership team.
• Good at mathematics with ability to demonstrating and publishing statistics reports and data insights to streamline processes. Sound understanding of project management tools and ability to collaborate with various departments to implement strategies for process improvements.
• Microsoft Certified Data Scientist in Azure Machine Learning with specializing in business data analysis and data analytics.
• Developed machine learning algorithms to gain operational insights on large distributed datasets.
• Technical expertise in information science, data preparation, exploratory analysis, and supervised and unsupervised modeling.
• Well versed with predictive algorithms including linear and non - linear regression and classification models.
• Created dashboards as part of data visualization using Tableau.
• Conducted preliminary data analysis using descriptive statistics.
• Experienced in cloud platforms like AWS, GCP etc.
• Utilized Azure Machine Learning Studio to eliminate anomalies, remove duplicates, and impute missing values.

TECHNICAL SKILLS

Scripts: R, Python (NumPy, Pandas, H2O), SQL, TSQL, NoSQL, HTML5, Java, C++

Tools: Tableau, Microsoft Visual Studio, R Studio, MS Power BI, SPSS, Palisade Decision tools, XL Miner, Crystal Reports, Jupyter Notebook, Microsoft Office Suite - Access, PowerPoint, Word, Excel, Kubernetes, Docker, D3, Apache Hadoop, GitHub, Azure Machine Learning Studio

Operating Systems: UNIX, Linux, Windows, Mac

PROFESSIONAL EXPERIENCE

Confidential, Dallas, TX

Data Scientist

Responsibilities:

• Engineer dashboards and reports to enable availability of statistical data under one roof to facilitate inter-departmental collaboration with executive leadership, sales, product, and marketing teams in evaluating consumer segmentation, revenue forecasting, and business decision making .
• Gather data from 12 different sources with 86 variables and compile data insights into cohesive stories and encourage stakeholder engagement and approval using statistical analysis and presentation.
• Research and develop production ready machine learning algorithms and programs by conducting channel, partner, and conversion performance analysis, and analyzing trends in asset utilization by using metadata such as asset type, category, and keywords.
• Utilize Tableau, Jupyter Notebook (Python -NumPy & H2O), JavaScript, R Studio, GCP, Azure Machine Learning Studio and other visualization and dashboarding tools to predict high-potential advertisement opportunities.
• Coordinate technical personnel, developers, data administrators, and management teams to develop applications in R, SQL, Python, Java etc.
• Enable HR professionals to make data-driven decisions to attract, manage, and retain employees to improve ROI focus and maximize employee productivity while positively impacting bottom-line returns.
• Spearhead appropriate methods to consolidate data from employee surveys, attendance records, multi-rater reviews, employee work, salary, segmentation, and promotion histories, demographic data, and personality and temperament data to build models to support HR initiatives for the organization’s strategic goals.
• Construct sophisticated data models, machine learning algorithms, tools and technologies to gain actionable insights in the form of dashboards, visualizations, and reports etc. Set up an ongoing process to implement and simplify continuous improvement and evaluation of models.
• Create data pipelines and services for quickly evaluating data management and governance. Deploy applications like machine learning and Artificial Neural Network (ANN) in production by leveraging deep understanding of data to improve data quality and usability in logistics.
• Develop Access-based models to consolidate forecasts, planning data, and truck arrivals allowing planners and schedulers to view all relevant information on a single screen to enhance interdepartmental decision making, consumer segmentation, and problem-solving capabilities.
• Identify and implement cost saving opportunities (CSOs) and measure contract performance to assess the value of new or renegotiated contracts to decrease accident risk. Utilized Advanced Excel, Tableau, Click and local GPO tools to proactively identify CSOs which helped cut down new recruitment which resulted in around 20% cost reduction.

Confidential

Data Analyst

Responsibilities:

• Gathered data from stakeholders, vendors, and market at-large to ensemble resources while maintaining cost-efficiency and viability. Facilitate technical support to enhance research and revenue forecasting.
• Optimized business strategies and problem solving techniques through rigorous review and overhaul of outdated processes to grow the company to one of the largest vendors of PPE with its own industry preferred and recognized brand in one and half years.
• Embraced leadership role to introduce, develop, and deploy 15 Zecura line of products after privately sourcing and hand-selecting each product from reliable and sustainable vendors from China and South Korea.
• Visited China and South Korea as business liaison, and implemented strict quality control and vendor management policies, standards, and structures.
• Utilized ERP software to manage purchases and plan material and resource allocation to further company goals and strategies into competitor markets.
• Utilized Agile methodologies to effectively communicate project insights and addressing effective cross-functional and inter-departmental needs to manage various facets of the business and realize strategic goals.