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Machine Learning Engineer Resume

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Somerset, NJ

SUMMARY

  • Certified Machine Learning Engineer with hands on experience in interpreting and analyzing data through ML & Statistical techniques and deriving meaningful insights to implement solutions in a fast - paced environment.
  • Around 5 years of overall experience with 3 years on Machine Learning Statistic Modeling, Predictive Modeling, Data Analytics, Data Modeling, Data Architecture, Data Analysis, Data Mining, Text Mining and Natural Language Processing (NLP), Artificial Intelligence algorithms.
  • Experienced in utilizing analytical applications like R, SPSS, and Python to identify trends and relationships between different pieces of data, draw appropriate conclusions and translate analytical findings into risk management and marketing strategies that drive value.
  • Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using R, Python and creating dashboards using tools like Tableau, QlikView.
  • Experience building solutions for enterprises, context-awareness, pervasive computing, and/or application f machine learning.
  • Proficient in data science life cycle, i.e., "Source • Clean • Explore • Communicate"
  • Experience in building data models by using machine learning techniques like Clustering Analysis, Market Basket Analysis, Association Rules, Naïve Bayes, Recommendation System, Dimension Reduction, Principal Component Analysis (PCA), Decision Tree and Neural Networks.
  • Working knowledge in building prediction models using Linear Regression Analysis, Logistic RegressionCorrelation Coefficient, Coefficient of determination techniques.
  • Good knowledge on statistical analysis techniques like Confidence Interval, Hypothesis testing, ANOVAConjoint analysis, sentiment analysis and semantic analysis.
  • Rich industry experience in Finance/Health care/Retail
  • Experience in Data Analysis, Data Migration, Data Cleansing, Transformation, Integration, Data Import, and Data Export.
  • Experience in managing code on GitHub
  • Adapt and deep understanding of Statistical modeling, Multivariate Analysis, model testing, problem analysis model comparison, optimization and validation.
  • Good Hands-on experience working with large datasets and Deep Learning class using TensorFlow and Apache Spark. Worked with Spark Core, Spark ML, Spark Streaming and Spark SQL.
  • Solid understanding of RDD operations Transformations & Actions, Accumulators, Broadcast variables, and job execution components including lineage graph, DAG, Dag Scheduler, task, Stages and Task scheduler.
  • Exposure to LSTM networks and Deep Reinforcement Learning . Team player with ability to communicate findings in data and policies with non-technical stake holders.
  • Strong experience in data visualization techniques for communication to client
  • Adapt and adhere to industry standards while working with multi-cultural teams.
  • Excellent communicator with strong leadership skills and ability to work independently.

PROFESSIONAL EXPERIENCE

Confidential

Machine Learning Engineer

Responsibilities:

  • Work independently and collaboratively throughout the complete analytics project lifecycle including data extraction/preparation, design and implementation of scalable machine learning analysis and solutions, and documentation of results.
  • Identified root causes of problems and facilitated the implementation of cost effective solutions with all levels of management.
  • Designed, implemented & deployed simple REST API service with Swagger and Python that provides SQL access for Ad-hoc analytics queries of AWS S3 Confidential data using AWS Athena and Glue. Results are returned in cursor-like chunks. Deployed application as a Docker container using Helm charts to a Kubernetes cluster.
  • Application of various machine learning algorithms and statistical modeling like decision trees, regression models, clustering, SVM to identify volume using scikit-learn package in R.
  • Worked on different data formats such as Confidential, XML, NLP to perform machine learning algorithms in Python.
  • Performed K-means clustering, Regression and Decision Trees in R and also worked with Naïve Bayes and skilled in Random Forests, Decision Trees, Linear and Logistic Regression, SVM, Clustering, Principle Component Analysis.
  • Partner with technical and non-technical, infrastructure and platform teams to configure, tune tools, automate tasks and guide the evolution of internal big data ecosystem; serve as a bridge between data scientists and infrastructure/platform teams.
  • Worked on Text Analytics and Naive Bayes creating word clouds and retrieving data from social networking platforms.
  • Pro-actively analyzed data to uncover insights that increase business value and impact.
  • Support various business partners on a wide range of analytics projects from ad-hoc requests to large-scale bcross-functional engagements
  • Prepared Data Visualization reports for the management using R and Tableau
  • Approach analytical problems with an appropriate blend of statistical/mathematical rigor with practical business intuition.
  • Hold a point-of-view on the strengths and limitations of statistical models and analyses in various business contexts and can evaluate and effectively communicate the uncertainty in the results.
  • Application of various machine learning algorithms and statistical modeling like decision trees, regression models, SVM, clustering to identify Volume using Scikit-learn package in python, MATLAB.
  • Approach analysis in multiple ways to evaluate approaches and compare results.

Environment: R, Python, MATLAB, Logistic regression, Naïve Bayes, Random Forest and Tableau.

Confidential, Somerset, NJ

Machine Learning Engineer

Responsibilities:

  • Implemented end-to-end systems for Data Analytics, Data Automation and Integration.
  • Responsible for data identification, collection, exploration & cleaning for modeling, participated in model development
  • Handled ad-hoc requests for business needs and provided high quality and accurate results.
  • Integrated with other departments to extract data from complex sources for reports and analysis.
  • Created Data Quality Scripts using SQL and Hive to validate successful data load and quality of the data.
  • Built standard reports for company presentation, provided ad-hoc query and analysis support, and created requirements document by interacting with customers.
  • Used Python and Spark to implement different machine learning algorithms including Generalized Linear Model, SVM, Random Forest, NLP, Boosting and Neural Network
  • Responsible for design and development of advanced R/Python programs to prepare transform and harmonize data sets in preparation for modeling.
  • Implement various statistical techniques to manipulate data (missing data imputation, principle component analysis and sampling) and build predictive models.
  • Writing detailed analysis plans and descriptions of analyses and findings for research protocols regulatory reports and healthcare manuscripts.
  • Developed MapReduce/Spark Python modules for machine learning & predictive analytics in Hadoop.
  • Implemented a Python-based distributed random forest via Python streaming.
  • Record and maintain meta-analyses and analyses of systematic reviews of medical literature
  • Successfully built models to predict glucose levels based on the meal plan followed by the Confidential using the Logistic regression
  • Created various types of data visualizations using Python and Tableau.
  • Worked closely with end-user clients to understand their reporting needs and provided necessary solution.

Environment: R, Python, Clustering, Regressions Analysis, Singular Vector Decomposition -SVD, Minitab, Confidential, and Tableau

Confidential San jose, CA

Data Analyst

Responsibilities:

  • Participated in data acquisition with Data Engineer team to extract historical and real-time data.
  • Provided statistical solutions for clients based on retail chains for Fast-Moving Consumer Goods (FMCG) and Consumer Packed Goods (CPG) goods.
  • Conducted Exploratory Data Analysis using R and carried out visualizations with Tableau reporting.
  • Analyzed high volume, high dimensional client and survey data from different sources using SAS and R.
  • Designed and implemented cross-validation and continuous statistical tests.
  • Developed, reviewed, tested and documented by using SAS macros and created Templates by using them for existing reports to reduce the manual intervention.
  • Created reports and dashboards to explain and communicate data insights, significant features, models scores and performance of new recommendation system to both technical and business teams.
  • Applied deep learning and 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.
  • Have experience working in Rapid miner studio and Python.
  • Used GIT for version control with Data Engineer team and Data Scientists colleagues.
  • Used agile methodology and SCRUM process for project developing.
  • Analytical implementation: Operations Analytics comprises store Scorecard, Cluster based analysis, Store and Resource productivity analysis, Growth-Trend analysis, Like-for-Like Store analysis.
  • Merchandising and Inventory Analytics - Merchandise Plan Performance, Category scorecardCategory tactics including Assortment planning, OTB planning, Buying Plan, Allocation planning and Promotion planning.
  • Customer Analytics - Demographic and purchase behavior segmentation, Customer Churn-Acquisition- Retention, Market Basket Analysis, Loyalty based analysis, RFM Scoring, Campaign Analysis, Customer Concentration Analysis and Customer Purchase Behavior Analysis.
  • Market Assessment (External Data Analytics) - Retailer vs. Competitor/ Market - Share & trend analysis, Channel Assessment, Retail format-based analysis, Pricing Comparison between regions, Event comparisons, Category mix comparison, Private label/ National brand analysis, Promotion mix, Customer buying behavior.
  • Documentation - provide functional documents to support Sales and Marketing teams.

Environment: R, Minitab, Multi-Class Logistics Regression Classifier, Boosted Regression Tree, Random Forest, Association Rules, Support Vector Machine, Clustering Analysis, Collaborative Recommended System, Time-series Analysis, Tableau, Excel-Miner.

TECHNICAL SKILLS

  • Programming and Scripting Languages R (shiny, ggplot2, dplyr, tidyr), C, C++, JAVA, HTML, Java Script, Python (numpy, scipy, scikit-learn, nlkt, gensim, keras), Scala
  • Databases SQL Server … MS-Access, Confidential … and Confidential, Hadoop, Spark, spark streaming, kafka,
  • Statistical Software R, SAS. Statistical Methods
  • Time Series, regression models, splines, confidence intervals, principal component analysis and Dimensionality Reduction, bootstrapping
  • BI Tools Confidential Power BI, Tableau, SSIS, SSRS, SSAS, Informatica 6.1
  • Tools and Utilities: SQL Server Management Studio, SQL Server Enterprise Manager, SQL Server Profiler, Confidential Office, Excel Power Pivot, Excel Data Explorer, Tableau, JIRA, Spark MLlib.
  • Machine learning Algorithms, Classification, KNN, Regression, Random Forest, Clustering(K-means), Neural Nets, SVM, Bayesian Algorithm, Social Media Analytics, Sentimental analysis, Market Base Analysis, Bagging, Boosting.
  • Cloud Computing Services AWS Athena, Glue, Confidential Redshift

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