Sr Data Scientist

• Lead all phases of the data science project lifecycle, including understanding complex business challenges, proposing and architecting technical solutions, data wrangling, data cleaning, exploratory data analysis, feature engineering, model selection and development, rigorous model validation, operationalization, and deployment of models, as well as clear presentation and documentation of results and actionable insights.
• Collaborate cross-functionally with business stakeholders, IT, and Project Management teams to design, develop, and deliver cutting-edge analytics solutions that have a measurable business impact.
• Leverage the latest technologies and methodologies, including Machine Learning, Artificial Intelligence, Natural Language Processing (NLP), and advanced Statistical Modeling, to solve challenging business problems
• Propose and develop new analytics solutions ("bottom-up innovations") that drive business value.
• Continuously research and apply state-of-the-art techniques and technologies (eg, Machine Learning, AI, NL) to improve model performance and drive innovation.

• A bachelors degree in a technical field (eg, Computer Science, Statistics, Mathematics, Data Science)

6+ years of hands-on experience in some of the following data science areas:

• Machine Learning, Predictive Modeling, Artificial Intelligence - experience in applying to real life use cases
• Building search or recommendation systems
• Text mining and Natural Language Processing
• Data analytics with multi-dimensional data
• Generative models

• Deep understanding of statistical methods and principles.
• Proficiency in Python, SQL, and Spark, working knowledge of Power BI, Excel, and PowerPoint.
• Experience with a variety of modeling techniques, including: Time Series, Random Forests, Clustering, Neural Networks, and Generalized Linear Models.
• Demonstrated ability to translate quantitative analysis into actionable business strategies and influence key stakeholders.
• A knack for working in a fast-paced environment and meeting project milestones.
• Ability to work collaboratively across product, data science and technical stakeholders.