Principal Data Scientist

Raymond James•Denver, CO

18h•Hybrid

About The Position

Analyze data to draw insights, patterns and conclusions that will enable the business to improve their products, services, processes or to develop new strategies. Must be adept at handling large, complex datasets and developing and applying robust analytical processes including data cleaning, data interpretation models, forecasting, pattern identification and trend analysis. As well as studying product and process-related data, may also be involved in studying environmental conditions in which the firm operates, competitor behavior, or other data sources. Requires knowledge of tools and methods including statistics, artificial intelligence and machine learning.

Requirements

Determine and analyze trends from data that is collected to assist in compiling reports that will help in decision-making.
Acquire, organize, protect and process data in order to fulfill business objectives.
Express user needs in terms of business requirements, identify current capability of the business to deliver, conduct build or buy analyses, and recommend preferred approach.
Acquire, organize, protect and process data in order to fulfill business objectives.
Bachelor’s: Business Administration, Bachelor’s: Data Processing, Bachelor’s (Required)
General Experience - 6 to 10 years

Nice To Haves

knowledge of tools and methods including statistics, artificial intelligence and machine learning.

Responsibilities

Lead the implementation of data and analytics strategy by developing a data insights integration approach and process aligned to key business processes and decisions.
Deploy advanced and predictive analytics across the company by identifying compelling business cases, establishing synergies with different technologies, defining implementation road maps, and consulting with various business departments to derive actionable recommendations and customize solutions.
Lead the performance and integration of highly sophisticated analytics to promote understanding of different business areas or critical business challenges/opportunities.
Define the road map for machine learning implementation, leading the model development life cycle, including data preparation, ingestion and integration, feature engineering, model engineering, and model evaluation.