SUMMARY

• I am a highly - driven and creative problem solver with the ability to leverage my unique skill-set and experiences. I come to the team as a broad generalist with comprehensive data science and quantitative modeling expertise.
• My educational background in engineering, up to the Ph.D. level, allowed me to master applied mathematics and multivariate statistics. It also allows me to develop software solutions rapidly, and learn new concepts and techniques quickly.
• My work experiences allowed me to sharpen my algorithm development skills and apply theory. I am passionate and willing to engage colleagues in team- driven problem solving.

TECHNICAL SKILLS

Python: OOP, NumPy, SciPy, Matplotlib, Scikit, Sklearn, multithreading, multiprocessing, profiling & code optimization, Idiomatic programming using Design Patterns

C++ (2011): Pointers & Memory Optimization, Code Optimization (Profiling), OOP, STL, Boost(Random Number Library, Math, Smart Pointers, BLAS, Thread, MPI)

MATLAB: OOP, Compiler (MCC and Deployment tool), MATLAB GUI, Memory & Code Optimization, C/MEX & C++/MEX, Simulink, Optimization, Curve Fitting, Image Processing, Statistics, Econometrics & Financial, Parallel Computing, Simulink, exception handling

Other: C, Fortran, VBA, SQL, Javascript, jQuery, PHP, Language interoperability (Fortran/C/C++ in VBA, C/C++ in Python, Matlab in C++), Google map API, MS Project

Regression: Linear, Nonlinear, Logistic, Boosted Regression Trees, Parameter Estimation

Classification: Neural Net, Decision Trees, KNN, SVM, Random Forest, Cluster Forest

Clustering: K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modeling

Time-series Forecasting: AR, ARMA, GARCH, Exponential Smoothing, Support Vector Regression

PROFESSIONAL EXPERIENCE

Confidential, New York, NY

Senior Data Scientist

Responsibilities:

• Develop Natural Language Understanding Algorithms for InsureTech applications
• Use Statistical Model of language and Machine Learning algorithms in end-to-end applications
• Develop AWS Big Data Pipeline for ETL and implementation of NLU algorithms using Serverless design
• Leveraged AWS Lambda, ECS (Docker Containers), EC2, Elastic Beanstalk, Elastic Load balancer, RDS, Elastic Search, AWS SQS, AWS Cloud Watch

Lead Data Scientist

Responsibilities:

• Built credit kiting fraud expert domain knowledge in 1 month
• Developed compliance ready Credit-card-kiting predictive model using consumer credit data
• Worked on ETL and pipe-lining into pre-processing, and analytics
• Used ensemble methods for feature selection on large feature set
• Used appropriate sampling methods, based on domain knowledge
• Built and validated predictive models (LR, RF, GBM, Deep Learning)
• Achieved low capture-rate, via post-model optimization FPR(0.18%), RCL(94%), ACC(99.8%)

CONFIDENTIAL, Houston, TX

Data Scientist

Responsibilities:

• Developed prototype code for time series prediction of consumer usage data
• Applied ML algorithms to time series data, support vector regression (SVR), etc.
• Built Sequence Learning App using Deep Learning Convolutional Neural Networks
• Used R & Python Interoperability to complete the above

Confidential, New York, NY

Data Scientist

Responsibilities:

• Developed Political forecasting models using Statistical & Machine Learning approaches
• Built predictive models for elections and ranking that out-performed traditional polls
• Research into predictive models for application to the referendum election
• Used predictive analytics for retail market insight & consumer preference
• Built a scalable platform for using IPython Cluster on heterogeneous VM, for CPU-intensive jobs
• Designed and partially implemented a process of integrating sampling and model predictions in a decision-theoretic feedback control framework.
• Built distributed Data Science automation back-end infrastructure that handles routing, data pre­processing, model execution, and Database communication using various concurrency models
• Contributed to the automation and standardization of the software cycle
• Statistical tools, Machine Learning, and infrastructure were implemented in R, Python, and C++
• Pushed for company adoption of a Micro-services design paradigm, and a DevOps Engineer
• Guided recruitment efforts for DevOps Engineer and Data Scientists
• Contributed to Survey design to support Data Science modeling Take-Away
• At MQR I fine-tuned my Data Science skill-set while solving business-relevant problems
• I learned new methodologies in Applied Statistics & Machine Learning
• I successfully prototyped algorithms, used open-source, & implemented in production level code
• I worked on scalable distributed computing design, a necessity in Data Science as datasets grow
• I successfully communicated technical material to non-technical members of the team

Confidential, Hawthorne, NY

Senior Engineer

Responsibilities:

• Built non-standard process equipment models Navier-Stokes, Heat, & Particle Balance equations
• Developed & validated macromolecule kinetic models and integrated with (above) CFD simulations
• Adapted models & solution methods (above) for novel emulsion and encapsulation applications
• Initiated machine learning & chemometrics methods for existing food science & process technology
• Managed and lead Associate Engineers in project based solutions to existing business problems
• Algorithm development for image processing using deterministic and neural network tools
• Automated feature extraction and database insertion using Machine Learning in C, C++ & MATLAB

Confidential, Brooklyn, NY

Teaching Assistant

Responsibilities:

• Taught in senior-level Chemical Engineering Laboratory CBE 4213 covering: Unit Operations, Process Control (PC & PLC), Transport phenomenon, Reaction Kinetics and Thermodynamics
• Doctoral Research in Surface Chemistry Based DNA Hybridization Kinetics (Modeling & Matlab)
• 6 years experience electro- analytical chemistry lab, studying DNA biosensors and microarrays
• Integrated NYC public school mathematics curriculum and doctoral research through mechatronics
• Led classroom in open-ended collaborative STEM design projects using electronic sensors