Data Science Manager Resume

SUMMARY

Over 13+ years of experience in IT industry - I have extensive experience using deep learning, natural language processing, and analytics as a service. This experience includes a skill mixture between a statistician, scientist, machine learning.
Data Scientist with special expertise in Machine Learning, Deep Learning, Text Mining, Statistics, Data Visualization, Data Cleaning, creating compelling stories as well as providing actionable insight. Proven success in analytical, problem solving and troubleshooting. Strong Communication skills, Strong working knowledge in structured, semi-structured and unstructured data, large data warehouse, multiple platforms including Hadoop File System, Azure, AWS, and mainframe.
Draw meaningful insights from data using machine learning techniques and statistics.
Transform business requirements into analytical models, design algorithms, develop data mining and reporting solutions that scale across a massive volume of structured and unstructured data.
Use Statistical Modeling and Machine Learning techniques - Linear & Logistic Regression, Naïve Bayes, Decision Trees, Random Forests, Clustering, SVM, Neural Networks, Principle Component Analysis, Bayesian, XGBoost, Forecasting and Recommender Systems.
Extensive experience in Text Analytics and Forecasting/Time Series, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using R and Python.
Utilize analytical applications/libraries like MicroStrategy and Tableau to identify trends and relationships between different pieces of data, draw appropriate conclusions and translate analytical findings into marketing strategies that drive value.
Worked and extracted data from various database sources like Oracle, SQLServer, DB2, and Teradata.
Expert working knowledge of AWS stack Redshift, Athena, PostGresSQL, AuroraDB etc
Expertise in all aspects of Software Development LifeCycle (SDLC).
Hands on experience on Spark Mlib utilities such as classification, regression, clustering, collaborative filtering, dimensionality reductions
Extensive experience working in a Test-Driven Development and Agile-Scrum Development.
Have worked on Hadoop, Spark, Hive, Hbase, Pig, HDFS, Sqoop.

TECHNICAL SKILLS

Business Intelligence
Predictive Analytics
Data Warehousing
Visualizations
Team Management
Business Requirements
Python - Anaconda, Numpy, Pandas, Scipy, Matplotlib,Pycharm
OpenCV
Tableau
Docker
AWS - Amazon web services
Azure
Google Analytics
Data Modeling
Social Intelligence
Machine Learning
Business Training
R
Computer Vision
MicroStrategy
Deep Learning
Git
Big Data
Behavioral Analytics
Data Integration
Scorecards
Time Series Analysis - Prophet
Spark - pyspsark
Mainframes
Text Analytics - NLP,NLU
Statistics - Descriptive and inferential
CICD

PROFESSIONAL EXPERIENCE

Confidential

Data Science Manager

Responsibilities:

Work cross functionally with stakeholders to ensure data-driven answers are provided and recommended
Develop technique/algorithms/measurement for research and analysis work
Lead the development and resolution of critical initiatives to support customer growth and provide critical decision support across Generics
Work with other teams to identify problems in different areas where data mining/machine learning/statistics can help
Communicate results; develop and maintain strong relationships with key stakeholders, partners and internal clients
Manage and build out a team of motivated Analysts, Data Scientists and or Statisticians
Drive business value; prioritize initiatives to ensure delivery of business value while continuing to build out the team’s core competencies

Data Science Manager/Architect

Confidential

Responsibilities:

Developed LSTM- CNN model in Python.
LSTM-CNN model consists of an initial LSTM layer which will receive word embeddings for each token as inputs.
The output of the LSTM layer is then fed into a convolution layer which we expect will extract local features.
Finally, the convolution layer’s output will be pooled to a smaller dimension and ultimately outputted as either a positive or negative label.
Facilitate design sessions with business, translate functional designs for visual presentation incorporating client feedback.
Organizes and shapes an engagement team’s strategy to drive success.
Develop and manage relationships across the whole client base, discussing benefits.
Drives key meetings and workshops to achieve the outcomes within the deadline.
Understands and utilizes the full range of facilitation methods and tools to run effective events
Lead the effort in building the data pipeline for Machine Learning models and Analytics

Environment: - Python- Anaconda, Pyspark, Ambari-Hive, AWS - Glue, Athena, Kinesis

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