Senior Data Scientist

About the Role

Key Responsibilities

• Develop custom data models to drive innovative business solutions.
• Build complex data sets from multiple data sources, both internally and externally.
• Conduct advanced statistical analysis to determine trends and significant data relationships.
• Build learning systems to analyze and filter continuous data flows and offline data analysis.
• Train algorithms to apply models to new data sets and validate models and techniques.
• Scale new algorithms to large data sets and combine data features to determine search models.
• Research new techniques and industry best practices, utilizing tools such as MySQL, Hadoop, Weka, R, MATLAB, and ILog.
• Analyze large data sets to develop custom models and algorithms for business solutions.
• Work on project teams to provide analytical support for projects like email targeting, business optimization, and consumer recommendations.
• Build large data sets from multiple sources to develop predictive algorithms, test and validate these algorithms, and apply them to large data sets.
• Train algorithms for future data application and relevant search results.
• Research industry trends and utilize up-to-date technology such as HBase, MapReduce, LAPack, and Gurobi.
• Collaborate with cross-functional partners to develop models of current state and identify improvements.
• Provide expert advice and guidance on information application and best practices.
• Support and implement business solutions by building relationships with stakeholders, identifying needs, monitoring progress, and recognizing improvement opportunities.

Requirements

• Master's degree or the equivalent in Statistics, Analytics, Computer Science, Engineering, or related field plus 2 years of data science experience or related experience.
• OR Bachelor's degree or the equivalent in Statistics, Analytics, Computer Science, Engineering, or related field plus 5 years of data science experience or related experience.
• Experience with object-oriented, procedure-oriented, and database programming languages including Python, Java, R, Linux Commands, and MYSQL.
• Experience developing supervised and unsupervised machine learning prediction models such as XGBoost, Random Forest, deep neural networks, K-means clustering, and graphical network models.
• Proficiency in Exploratory Data Analysis, statistical analysis, hypothesis testing, and statistical inferences.
• Experience with data processing including text content processing, web scraping, data mining, and data frame processing with user-defined functions.
• Ability to create features and develop feature engineering to improve prediction models using Python.
• Experience coding and reviewing on platforms like Google Cloud Platform, Microsoft Azure, and GitHub.
• Knowledge of model assessment and validation techniques including Chi square, ROC curve, and root mean square error.
• Skills in data visualization tools such as Tableau, Tableau Prep, Power BI, Gephi.
• Experience with complex data transformation, calculation, and cleaning in Tableau Prep.
• Application of network analysis packages like NetworkX in Python, and experience with PySpark and sklearn for model development and data analysis.
• Proficiency with visualization libraries such as Matplotlib, Graphviz, Plotly, GGplot in Python, and R.
• Experience with geospatial packages including GeoPandas, Shapely, Pyproj, Geoplot, and Rasterio for spatial data processing.

Nice to Have

• Any amount of experience with the required skills will be accepted.

Qualifications

• Master's or Bachelor's degree in relevant fields with corresponding years of experience.