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Data Scientist Resume

3.00 Rating

NY

OBJECTIVE

  • Adopt dynamic and upcoming business analytics and data science best practices being part of challenging and innovative initiatives in financial and utilities domain and, grow with the expectation of my organization.
  • Build Thought leadership and COE by collaborating technologies, data driven decisions and processes in Enterprise as well as startup environment. Innovate and contribute in state - of-the-art decision modeling patterns and visualization to aid Strategic decisions.
  • Aligning business-driven objectives with the solutions and analytical standards, delivering actionable insights by developing reusable tools and platforms
  • Contribute towards strong team culture and leadership practices to drive innovations

SUMMARY

  • 14+ years of Industry experience in Managing strategic business functions, Developing and Maintaining Business analytics and ERP implementations.
  • 5+ years of experience in designing and developing data analytics solutions, data centric integration.
  • Work with business domain experts and application developers to identify data relevant for analysis / mining and,develop new predictive / analytical modeling methods and/or toolsin Utilities domains, Financial (Capital Market & Asset Management), Retail.
  • Experience with developing and planning data mining and analytics projects in response to strategic business needs. Involved in diagnosing and Resolving predictive / analytical model performance issues, monitoring analytical system performance and, implementing efficiency improvements.
  • Experience in hypothesis testing, multivariate testing (A/B testing) and optimization algorithms, descriptive, exploratory, inferential, predictive modeling of the given scenario along with data modeling and recognizing key performance indicators (KPI).
  • Adept in predictive modeling, machine learning and data mining with extensive use of R.
  • Experience in predictive analytic procedures used in supervised learning (Regression, Neural Networks, Decision trees), unsupervised learning (Clustering-k-Means and Hierarchical, PCA).
  • Experience in trend analysis and simulation using Time Series, Monte Carlo and Marko’s Chain techniques
  • Experience in data aggregation and reduction techniques of large data sets with high performance and parallel computing in R for high performance analytical projects
  • Proficient in R-Studio, Tableau, MS Excel tools used in data analysis/ mining, various analytics and data visualization implementations.
  • Conversant with SQL/ PL SQL and RDBMS. Contributed in data definitions for new database file/table development and/or changes to existing ones as needed for analysis and mining purpose.
  • Working knowledge of Big Data Analytics, Hadoop ecosystems (Hadoop, Hive) and Spark, integration with R.
  • Working knowledge on Decision modelling techniques and frameworks. Building Monte Carlo simulation model using Crystal Ball app.
  • Worked in SAP Full life cycle implementations, Rollouts and Support projects. Experienced in configuration and customization of FI (GL, AP, AR, AA) and CO (CO-CCA, IO and CO-PCA), design and development, customization and implementation of Data Migration specifications with BDC, Legacy System Migration workbench (LSMW) for data uploads for various SAP Projects.
  • Four years of industry experience in leading R&D and production of television Components, reliability testing’s & quality compliance and material management
  • A self-motivated and business-savvy data scientist with broad experience in collaborating with focused groups within business and IT ecosystem to avoid redundancy, establish best practices and guidelines for selecting, developing, and implementing BI and analytics projects.
  • Conversant with Project Management deliverables and SDLC phases - Waterfall and Agile
  • A self-starter, team player, excellent communicator, prolific researcher and organizer with experience in managing and coordinating on-shore and offshore teams.
  • Expert technical documentation skills. Strong interpersonal and communication skills (both written and oral); ability to communicate with people in a wide variety of areas and at various levels from technical specialists to senior management.

TECHNICAL SKILLS

Roles: Data Scientist, Data Analyst, Project Coordinator, Techno-functional consultant, System Analyst

Statistical Tools: R-Studio, Stata, Rapid Miner

Programing Language: R, Python, VBA

BI and Visualization: Tableau Desktop 8.3, R reporting tools

Database (RDBMS) & Big Data: MS SQL Server, MS Access, Oracle, Hadoop, Hive

Operating Systems: Linux, Mac OSX and Windows

Office Tools: MS Office (Excel, Word, PowerPoint & Access)

PROFESSIONAL EXPERIENCE

Confidential, NY

Data Scientist

Responsibilities:

  • Work with key stakeholders and energy consultants to define, develop, deploy, and coordinate data acquisition and preparation, statistical analysis, interactive data visualization and predictive models to build automated machine learning and score metrics support of business and data analytics for energy efficiency and demand response engagements.
  • Conducted Exploratory Data Analysis using R and carried out visualizations with Tableau reporting.
  • Determine data reduction methodologies for dealing with noisy data (correlation matrix, principal components analysis, clustering), missing values, and outliers
  • Determine appropriate models and default parameters including decision trees, random forests, logistic regression, K-Means, and Nearest Neighbor, etc.
  • Work with Data Analytics team to develop time series and optimization models and aid tactical and strategic guidance on optimizing generation and consumption portfolios which are consistent with variable pricing mandates.
  • Develop BI and Analytics reports on demand and supply trends and KPIs and extracted test data from proto-type equipment.
  • Identify the types using clustering and build a predictive model for the quantities of flexible loads
  • Build predictive models class probability estimation, based on facilities and building characteristics, of the adoption of solar and power storage technologies.
  • Applied Time series ARIMA method to forecast renewable energy generation and consumption trends.
  • Built a predictive model to quantify structural reduction in net load as a function of the presence of solar PV, facilities and/or building characteristics and weather for a particular zone.
  • Derive efficiency benchmarks for different clusters of facilities and create report templates.
  • Build predictive models, based on facility characteristics of the adoption of solar and power storage technologies.
  • Coordinate with data analysts / architects to aggregate and scrub large datasets to prepare data and structure database, which satisfies reporting, and analysis needs.
  • Apply decision modelling frameworks (linear nonlinear) and derive various trends to analyze Energy asset portfolios, consumption and losses with attribution to provide feedbacks on demand flexibility strategies
  • Analyze real-time energy utilization performance, at both the portfolio and operation level, develop optimization analytics models facilitating schedule management and incentive maximization
  • Knowledge elicitation in collaboration with business analysts and subject matter experts to gather business and data requirements, and work with database professionals through the data preparation phase.
  • Train non-technical business personnel to run / score models and generate reports.

Environment: R, Tableau, Stata, MySQL, Microsoft Excel

Confidential, SFO, CA

Data Scientist

Responsibilities:

  • Support Asset management team with quantitative analysis and performance benchmarking on portfolio composition with specific allocation recommendations across asset classes and products.
  • Developed time series analysis and simulation models to access the effectiveness of debt funding strategy. Identify assets that are rich or cheap by assessing model results against recent or expected market developments.
  • Develop Analytics models to optimize custom benchmarks utilized in assessing the performance of investment strategies, which are personalized against specific funds and investors goals
  • Manipulating and analyzing complex, high-volume, high-dimensionality data from structured and unstructured data sources on securities, bonds, funds and other asset portfolios.
  • Automate return based and portfolio- composition based style analysis to assess the consistency of any fund’s performance overtime.
  • Identify, summarize and report on financial trends that impact SKI using data from various tools
  • Prepare and maintain programs and documentation for analytic models.
  • Conducted defined quantitative and qualitative research projects independently and communicates research results to internal and external stakeholders.
  • Analyze complex patterns of asset performance data through advanced data mining techniques. Worked on analysis of complexes, high-volume, high-dimensionality data with R. Managed development of reusable frameworks and tools to perform term structure analysis and generate trade ideas, access investment / funding guidelines, market risk policies, liquidity risk policies and determine capital policies.
  • Documented and validated all models for the Risk Committees before and after operationalization. Worked with IT to assess and implement new software and technology
  • Coordinate with data analysts / architects to aggregate and scrub large datasets to prepare data and structure database, which satisfies reporting, and analysis needs.
  • Apply analytics/ machine learning algorithms to automate portfolio collection and aggregation process, access to appropriate market information and utilization of different pricing methodologies to estimate fair value.
  • Analyze real-time trading performance, at both the portfolio and security level, develop optimization and term structure analytics models facilitating hedge recommendations for portfolio management to improve return/risk profile
  • Produced bond and attribute coverage of financial instruments present in the database on regular basis
  • Developed modules for enhancement of time-series and term-structure functionalities
  • Presented application features to various audiences in the bank by reproducing trade ideas from research journals
  • Developed scripts and ad-hoc tests to ascertain data validity and correct attribute calculation
  • Performed statistical and predictive analysis on corporate market data to identify trends, buy-sell opportunities
  • Performed correlation and time-series analysis to recommend pairs trading strategies to management
  • Performed advanced statistical analysis like scenario analysis and back testing as per requirements
  • Supported business teams to develop profit and loss report for collateral desk detailing profit at counterparty level, trade level, book level and desk level granularities

Environment: R, Tableau, Excel, SQL Server 2000

Confidential, Kansas City, MO

Data Scientist and Predictive Modeler

Responsibilities:

  • Create innovative of algorithms behind a variety of services ranging from Propensity to Chum and Upsell Customer Effort Scoring.
  • Perform ad hic statically, data mining and machine learning analysis on complex business problems
  • Collaborate and work closely with various Business, IT and Marketing teams
  • Develop and design advance predictive analysis models using R
  • Working with IT to integrate new technologies into workflows (ex: Hadoop)
  • Work with large sets of data
  • Projects were focused on Enhancing Customer Engagement, minimize fraud, solving for Chum and other detractors and Reducing Business Costs
  • Prepared Product affinity analysis to understand the customer and their needs better.
  • Optimized customer classification by modeling customer segmentation using demographic attributes, geographic attributes and purchasing patterns.
  • Assisted optimizing marketing mix model and worked closely with the marketing department to get the most out of the model performance
  • Research data to improve customer engagement. Finding and fixing customer pain points
  • Research data to predict the probability of customer actions & looking actions at certain marketing and incentives aspects of the retail business
  • Involved with statistical domain experts to understand the data and worked with data management team on data quality assurance
  • Skilled in Extracting, Loading and Transforming (ETL) process from Oracle or MySQL database
  • Integrated data sets from various sources: customers base, call center inbound and outbound calls, campaigns, BSS&OSS, CDR, plans & prices
  • Performed data munging/data wrangling on that includes transformations, merging, sorting, detecting missing values, outliers, distributions of the data
  • Documented and submitted reports on descriptive statistics and graphs of predictor variables
  • Performed data balancing to balance out the ratio of subscribers for churn versus active subscribers
  • Traced and analyzed customer churn pattern over the historical data of 6 months
  • Carried out logistic regression, analyzed coefficient estimates, probabilities of predicted and observed responses and concordance and discordant pairs
  • Carried out forward, backward, subset and stepwise variable selection to obtain best model giving high C-statistic/concordance percentage
  • Performed k-Means clustering in order to understand customer attitudes, behaviors, actions and segment the customers based on different levels of churn risk
  • Analyzing RFM (Regency, Frequency and Monetary) analysis framework
  • Performed decision trees and cost complexity pruning to have higher sensitivity and accuracy
  • Carried out Neural Networks and compared error through resilient back propagation and propagation
  • Models were compared based on validation accuracy percentage, misclassification rate, ROC curves, AUC, cumulative lift curves
  • Understanding the propensity scores given to the customers who are likely to churn
  • Translated analytical model findings to business insights and presented them to non-technical audiences.
  • Identified key KPI’s that improve customer retention, reduce the marketing costs, effective targeted services and increased ROI
  • Created data visualizations on churn rate, fraud patterns and product retailing performance, at each geographical location, relationship between predictor and response variable, neural network graph

Environment: Oracle, R, CSV/ Excel, Hadoop, Hive

Confidential, New York, NY

Data Analyst and BI Consultant

Responsibilities:

  • Involved in fetching data from multiple ERP/NON ERP system
  • Aggregate relevant information and designed dashboard for real time trends
  • Develop modules for price performance, asset performance and alerts.
  • Build predictive models class probability estimation, based on house and household characteristics, of the adoption of solar, electric vehicle and power storage technologies.
  • Build predictive models Network analytics for the adoption of the above technologies based on peer influence.
  • Co-ordinate with domain consultants and technical team for product development and customization.
  • Coordinate with client SME’s to define the blueprint for data and service integration.
  • Understanding the existing IS Utility landscape, and define the integration touch points.
  • Coordinate with client SME’s to define the blueprint for data and service integration.
  • Preparation of development functional specification and development of BI reporting.
  • Develop BI and analytics reports on demand and supply optimization trends and KPIs
  • Worked with integration of product with utilities backend
  • Used SQL queriesto extract utilities usage data from Oracle 11g database to lower environment for data analysis and model building.
  • Performed outlier detection, imputation for missing data, cleaning, transforming, and normalization.
  • Performed literature review and research to identify historical patterns and methods applied to the data.
  • Developed conceptual models, followed by logical models based on the data collected.
  • Clustering to obtain similar type of data based on location and availability of alternate energy source.
  • Developed various non-linear regression models to study the relationship between variables.
  • Created tableau dashboard to present the results and offered recommendations based on the analysis.

Environment: R, PostgreSQL, Hadoop, Hive, SAP PI/ IDOC

Confidential

SAP Functional Consultant

Responsibilities:

  • Engaged as FICO Team Member in offshore for Cluster 7 Rollout.
  • Worked in Key Data Structure Mapping.
  • GL configuration, Country Tax code, Cash Journal set up, Company Operation, Chart of Account.
  • Accounts Payable - Configured and Customized Vendor master records, Account Groups, Number Ranges, Tolerance Groups, Reason Codes, account Determination.
  • Accounts Receivable - Configured and Customized Customer Master Records, Account Groups, Number Ranges, Tolerance Groups, Reason Codes, Account Determination.
  • Configured and Customized Asset accounting module - copied country specific Chart of depreciation, configured depreciation areas.
  • Configured controlling area settings, Number ranges and maintained versions.
  • Configured and Customized general controlling, cost and revenue element accounting, cost center accounting.

Environment: SAP R/3 4.7

Confidential

ABAP Consultant (Technical Lead)

Responsibilities:

  • Leading a team of twelve members for design and development of the modules
  • Working closely with business analysts and module leads to carry out gap analysis and capturing functional requirements.
  • Preparing Design specification of the system for development, customization of Forms and reports in various modules.
  • Coordinate with the QA teams during Integration testing and UAT phase
  • Plan and deliver customization and development of following modules in multiple releases
  • Planning v/s Actual Production that compares the trends.
  • Vendor Performance Report that lists Vendor Name, Materials Supplied, Planned & Actual Delivery Times, Payments Terms and Prices.
  • Report to display a list of Purchase Requisitions with details like MRP Controller, Release Date and Unit of Measure along with standard details.
  • Missing quantity list that gives the difference between sent and received quantities during Stock Transfer.
  • Material Master that outputs Material Valuated Stock grouped by Material type and Plant. The output shows Material No., Storage Location, Unit of Measure and Description in addition to group totals. Data was extracted from MARA, MARC, MARD, MAKT, EKKO, EKPO, MKPF, MSEG and T001W.
  • Using the BDC program uploaded the BOM, material master data from legacy system to SAP using flat files.
  • Uploaded Customer, Pricing, and purchase info records from legacy system to SAP
  • Developed BDC programs to fix data.

Environment: ECC 6.0

Confidential

ABAP Consultant

Responsibilities:

  • Generated and modified classical Reports, which will furnish sales order information or purchase, order information.
  • Generated a classical report delivering fixed assets details depending on company code.
  • An interactive report on sales cycle history’.
  • Developed a customized report for Customer Invoices.
  • Developed an Interactive ALV report, which delivers ‘Sales order information’ OR ‘Delivery information’ OR ‘Invoice information’.
  • Developed BDC program to change customer details and using transaction XD01 using call transaction method.
  • Developed a program to build BDC session to update basic data in the material master record using the transaction MM02.
  • Using the standard data conversion program uploaded the material master data from legacy system to SAP R/3 using flat files.
  • Design and coded BDC program on ‘customer master change’ using the transaction code XD02.

Environment: SAP R/3 4.6

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