Data Scientist

Bachelor's Degree in STEM (science, technology, engineering, math) related field or a similar quantitative analytics field. Up to 4 years of professional experience including exposure to data science concepts and computational tools (i.e., Python, Spark (PySpark), SQL, Hadoop, etc.). Professional, internship or project-based experience preferred. Familiarity with varying database structures and experience working with large datasets. Exposure to data visualization technology and capabilities (i.e., Power BI, Tableau). Exposure to a variety of data products that will complement data preparation, algorithm models and visualization development (i.e., Panda Libraries, Jupyter Notebooks, Power BI, etc.). Intermediate technical and analytical abilities with programming skills in a language such as Python. Basic understanding of the techniques to collect, organize, blend, cleanse and synthesize large volumes of disparate data. Ability to effectively communicate "data stories" to other non-technical business partners. Develop knowledge and then utilize a range of data science tools and techniques including Azure Cloud Data & Advanced Analytics tech stack, Spark via Databricks, Azure Machine Learning and PowerBI/Tableau. Participate in the development of documentation; coordinate closely with team members and leadership to communicate project statuses and initiatives. Design new data science solutions and implement research ideas and trading algorithms to drive insights consumed by analysts and portfolio managers. Work in an agile, collaborative environment, partnering with other data scientists, data engineers, and data analysts of all backgrounds and disciplines to bring data and analytics to life. Assist development of data pipeline programs and perform analysis on alternative and traditional datasets to develop investment factors and insights using machine learning and quantitative methods. Identify and develop custom data models and the appropriate algorithmic methods (regression models, classification, tree-based methods, text mining, natural language processing, unsupervised learning such as clustering, etc.) to support all stages of the data science lifecycle cycle. Build compelling, clear, and powerful visualizations that are useful and appealing to users. Prepare written and verbal communications along with preparing and delivering data science artifacts (abstract, data sources / data dictionaries, code library, research / findings, modeling / deployment report, new ideas/next steps). Responsible for staying on top of analytical techniques such as machine learning, deep learning and text analytics as they rapidly evolve.

Federated Hermes is committed to providing equal employment opportunities in all aspects of employment to qualified individuals without regard to the following criteria: race, color, national origin, religion, sex, pregnancy, sexual orientation, gender identity or expression, mental or physical disability, age, familial or marital status, ancestry, military status, veteran status or genetic information as well as any other prohibited criteria under any local, state or federal law applicable to Federated Hermes. As part of the firm's equal employment opportunity statement, Federated Hermes will also take affirmative action to ensure that minorities, females, veterans, and qualified people with disabilities are considered for employment and promotional opportunities.