lead data scientist

Key Skills

Aartificial intelligence, Algorithms, Apache Hadoop, Apache Hive, C Programming, Data pipelines, Data science techniques, HTML, Java Programming, JavaScript, Machine learning techniques, MATLAB Programming, Python Programming, R Programming, Scala Programming, SPARK Programming, SQL

Job Description

Lead the development and application of mathematical and statistical theory and methods to create long-term forecasting algorithms that solve business problems in retail with a deep understanding of decision science methodologies. Transform how retail operations work by automating many of the processes that currently require manual activity and decision making through mathematics and statistical models. To collect, organize, analyze, interpret, and summarize data large amounts of time-series data to generate key insights leading to new innovative applications. Analyze complex and rich data, evaluate business problems, develop problem statements, define metrics, and drive feasibility studies. Perform data exploration tasks, extract insights and create story boards from data analysis. Present clear and accurate root-cause analysis to peer scientists and leadership. Build training workflows for machine learning algorithms on millions of data points and create algorithmic solutions including data understanding, feature engineering, model development, validation, and model deployment. Lead and develop large-scale implementations of machine learning models and algorithmic solutions using rich retail data sources. Review model performance and identify enhancements to improve models. Collaborate with global AI team, scientists, engineers, and business partners to identify and lead new opportunities. Utilize expertise in machine learning, probability theory and statistics, optimization theory, deep learning, data pipeline engineering, distributed systems, database architecture, linear programming, and data mining skills, along with SQL, R, Python, Spark, C, JavaScript, Scala, Hadoop, Hive, HTML, Matlab, Java, and Command Shell programming. Advocate best software engineering practices and able to prototype individual components of data science solution. May telecommute from any location in the U.S.

REQUIREMENTS: At least a Master’s degree in Mathematics, Statistics, or a closely related quantitative field, and at least seven (7) years of experience as a data or machine learning scientist (any title) including engaging in advanced machine learning activities (regression, clustering, and forecasting); driving the development and implementation of analytical and data science modeling

solutions by using techniques including data mining, machine learning or text mining; utilizing mathematical concepts, algorithms and computational complexity; collaborating with colleagues to bring business value to the organization; presenting findings and reports to stakeholders; and using Python, SQL, R, SAS, risk analysis, and data manipulation. Must include at least two (2) years engaging with large volume of data and solving business problems; working on providing data science solutions including problem statement, feature engineering, model development, evaluation and testing, and deployment to a production environment; collaborating with engineering team to address any issues in production pipeline; productionizing, auditing and monitoring data science models in online environment; working with Hive or Hadoop, machine learning, text mining and Spark.

In the alternative, the employer will accept at least a doctorate (PhD) degree in Mathematics, Statistics, or a closely related quantitative field, and at least four (4) years of experience as a data or machine learning scientist (any title) including engaging in advanced machine learning activities (regression, clustering, and forecasting); driving the development and implementation of analytical and data science modeling solutions by using techniques including data mining, machine learning or text mining; utilizing mathematical concepts, algorithms and computational complexity; collaborating with colleagues to bring business value to the organization; presenting findings and reports to stakeholders; and using Python, SQL, R, SAS, risk analysis, and data manipulation. Must include at least two (2) years engaging with large volume of data and solving business problems; working on providing data science solutions including problem statement, feature engineering, model development, evaluation and testing, and deployment to a production environment; collaborating with engineering team to address any issues in production pipeline; productionizing, auditing and monitoring data science models in online environment; working with Hive or Hadoop, machine learning, text mining and Spark.