Position: Lead  Data Scientist
Category: Data Science
English level: upper
Years of experience: 8

July 2017 - Now		SoftServe
Position: Data Analyst / Data Scientist
"	Creating innovative and efficient products that address and solve Client's business problems in Retail.
"	Working with alongside business and technical stakeholders to identify solution requirements, business goals, and expected outcome.
"	Appling advanced statistical, data mining and artificial intelligence techniques to design complex analytical solutions, solving difficult problems.
"	Delivering rapid prototypes and proof of concepts that demonstrate real value. Using Shiny R for building demo application and PoC.
"	Analyzing raw business data, drawing conclusions & developing recommendations. Manipulating, cleaning & processing large, complex datasets using Python, R, Spark,… 
"	Building solutions for retail, for example price optimization, recommender systems and profiling customers.
"	Analyzing, finding anomalies and predicting time-series data.
"	Interpreting results of models and showing them using visualization techniques.
Core Competencies:
"	Strategic Thinking: Identifying opportunities in large, rich data sets, creating and implementing data-driven strategies that fuel growth including revenue and profits.
"	Modeling: Design and implement statistical / predictive models and cutting-edge algorithms utilizing diverse sources of data to solve business problems (for example optimize for gross profit, predict demand and price elasticity...). 
"	Analytics: Use statistical methods to identify trends and relationships between different pieces of data, draw appropriate conclusions and translate analytical findings into strategies that drive solving retail problems.
"	Drive Enhancements: Develop tools and reports that help users access and analyze data resulting in higher revenues and margins and a better customer experience.
"	Communications: Capable of turning dry analysis into an exciting story that influences the direction of the business.

Skills and Tools:
"	Statistical Methods: regression models, hypothesis testing and confidence intervals, principal component analysis and dimensionality reduction.
"	Machine Learning Algorithms: classification, regression, clustering, tree based models, feature engineering, ...
"	Deep Learning Algorithms: ANN, CNN, RNN, …
"	Mathematics: theory of probability, statistic, econometrics, mathematical and complex analysis, …
"	Software and Programming Languages: Python (scikit-learn, numpy, scipy, pandas, gensim, keras, tensorflow, …), R, R Shiny.
"	Big Data Technologies: Spark, Hadoop, MapReduce, Azure, AWS.
"	Version control system: Git, SVN.
"	Database: SQL, NoSQL.
Objective: a position of data scientist or data analyst in your company. Providing data-driven, action-oriented solutions to challenging business problems.