Senior Data Scientist Resume

TECHNICAL SKILLS:

Programming languages: Scala, Python, R

NLP/ML technology: MLLib, Gensim, Numpy, Scikit - learn, Spacy

Deep Learning: Keras, TensorFlow, DL4J

Big Data & Cloud: AWS, Google Cloud, Azure, Hadoop, Spark

Database: ElasticSearch, LevelDB, MySQL, MariaDB, PostgreSQL

Visualization: RStudio, R Shiny, matplotlib, Seaborn

PROFESSIONAL EXPERIENCE:

Confidential

Senior Data Scientist

Responsibilities:

Developed original algorithms for identifying and scoring customer intent and propensity to automate and personalize customer interactions.
Developed an end-to-end automated machine learning pipeline for structured data invovling fully automated: feature engineering, feature selection, feature validation, model selection and model validation.
Developed several approaches to “looks like” customer profiling (Know Your Customer modeling) including fast tests based on statistical tests and in-depth analysis using Deep Learning models.
Developed solutions for guiding and optimizing customer journeys using intent analysis. Simple methods use multiple classifiers, user-user similarity, and graph clustering while complex methods use Deep Reinforcement Learning models.
Researched and developed POC solutions for advanced Deep Learning predictive models using RNN, Sequence-to-Sequence, LSTM, and Reinforcement Learning
Developed a set of neologism analytics. Used for nowcasting event detection in streaming data, and metrics to determine when a language model needs to be rebuilt from scratch
Developed an Active Learning classifier for semi-supervised tagging of news articles
Developed a novel distributed approach to Query Expansion using Spark, a custom Word2Vec implementation and a Golang backend for fast, scalable, concurrent query retrieval
Developed a parameter server running on top of Spark for parallelising sequential machine learning algorithms
Developed a generalizable, Big Data scalable approach to descriptive and predictive analysis of customer data
Researched prescriptive analytics approaches for journey-based decisioning to suggest actions that might be delivered to customers to increase conversion
Applied Deep Learning to classification problems (CNN and LSTM neural networks)
Built models to detect risks of credit, liguidity and fraud within payments systems
Created a payments technologies stock index

Confidential

Data scientist

Responsibilities:

Identified discriminative sentiment terms using various correlation measures
Automatically derived term weights to improve lexical weights
Classify texts into news and not-news
Classify texts according to stance (neutral vs opinion, support vs oppose)
Compute probability that a text is about a topic of interest
Used a pseudo-parallel Markov Chain Monte Carlo approach for efficient approximate inference of Bayesian posterior
Developed a non-parametric Bayesian approach to topic modeling using a Dirichlet Process to infer an unbounded number of parameters
Using TFIDF weights over a sliding window to discount overly common terms while boosting significant terms
Apply automatic labels to the 'latent' topics
Used TensorFlow to create distributional semantic model of text
Researched word embeddings to discover term analogies within a corpus for more intuitive information retrieval

Confidential

Data scientist / NLP researcher

Responsibilities:

Wrote ETL and other big data tasks to run on Amazon Web Services EMR clusters using Hadoop, Hive and Pig (with custom UDFs).
Created an algorithm to identify content similarity.
Collaborated on an entity extraction engine using a graph created using Freebase topics, boosting the performance of the content similarity engine.
Created model of writing style to improve recommendation based on story similarity.
Lead a Gamestorming session to aid business innovation.
Designed and created a fast language detection engine capable of identifying language with a high degree of accuracy.
Reverse-engineered Coh-Metrix, creating an engine able to extract cohesion and coherence metrics from texts.

Confidential

Sessional lecturer

Responsibilities:

Responsible for creating and delivering lectures, making worksheets, assignments, tests and final exam.
Administered a Coursera online component of the course.
Supervised 5 teaching assistants, assigned duties and held grading meetings.

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