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Data Scientist

Location New York, United States
Posted 14-September-2021

A large retail client is looking for a Data Scientist in NYC. You will support the Senior Manager, Data Science in owning the overall customer and marketing application data architecture, automation of data collection, identification of net new data needed to support business use cases and advise on data science approaches, pilot design and analytics.

4+ years of demonstrated ability in a similar data scientist role

Degree in data science, computer science, statistics, economics, mathematics, or a similar quantitative field

Significant experience with advanced statistical methods and machine learning

Significant experience with customer analytics, including crafting success metrics, running power analyses, determining statistical significance, and presenting findings with clear product recommendations or implications

Previous experience working with a team of data scientists and data engineers building machine learning tools in production and at scale

You have hands-on experience synthesizing insights from data using tools such as Python, R, BigQuery, SQL, Tableau or Microstrategy

Knowledge of relevant languages/toolseg advanced Python(pandas, numpy, sklearn, scipy, statsmodels), SQL (analytic functions, subqueries, etc), Spark (inclMLlib)-using pySpark & Spark SQL, Git & Github, AWS EMR, Airflow, AWS SageMaker, Dataiku or similar platforms

Ability to apply the following modeling approaches/classes of algorithms to business problems eg regressions, K Nearest Neighbors, Random Forest, GBM, Neural Networks, boosted trees, LDA, collaborative filtering, Bayesian

Strong communication skills and value building strong relationships with colleagues and partners as well as the ability to explain sophisticated topics in straightforward terms

Youre a compelling storyteller who can communicate in succinct and inspiring ways to audiences with varied data science experience to influence real world marketing applications or decisions

Comfortable working with distributed teams across multiple offices and timezones

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