Data Scientist

Shine Associates, LLC

23h

About The Position

As a member of the cross-disciplined Strategic Analytics team, the Data Scientist will play a crucial role in analyzing complex datasets, uncovering valuable insights, and providing data-driven solutions to drive business growth and innovation. The data scientist will work closely with business leaders to improve operating performance and foster data-driven decision-making processes across the firm’s investment and operating verticals. The successful candidate will bring curiosity and problem solving skills and able to transition from working with business and technology professionals to deliver actionable results.

Requirements

Bachelor's degree or higher in Computer Science, Statistics, Mathematics, or a related field.
Proficiency in programming languages such as SQL and Python or R. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools (e.g. Power BI).
Strong analytical and problem-solving abilities, with keen attention to detail. Ability to identify patterns, trends, and insights from complex datasets.
Strong written and verbal communication skills. Ability to translate technical findings into actionable insights and present them effectively to both technical and non-technical audiences.
Ability to work effectively in cross-functional teams. Strong interpersonal skills and the ability to collaborate with individuals at all levels of the organization.
Ability to thrive in a fast-paced and dynamic environment. Willingness to learn new technologies and methodologies to stay ahead in the field of data science.
2-3 years’ experience in a data science or analytics role, applying statistical analysis and machine learning techniques to real-world problems.

Nice To Haves

Knowledge of Azure ML and Snowflake (Snowpark ML) a plus.

Responsibilities

Data Analysis and Modeling: Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets. Apply data preprocessing, feature engineering, and predictive modeling to extract meaningful insights and develop robust models.
Problem Solving: Contribute to the solution of business challenges through innovative solutions using data-driven approaches. Collaborate with stakeholders to define problem statements, formulate hypotheses, and design experiments to validate solutions.
Data Visualization: Create compelling visualizations and dashboards to communicate complex findings in a clear and concise manner. Present insights and recommendations to technical and non-technical audiences, including senior management, to drive informed decision-making.
Machine Learning Development: Support the creation and deployment of machine learning models, leveraging both traditional statistical techniques and cutting-edge algorithms. Optimize models for performance, scalability, and interpretability.
Data Exploration and Cleansing: Work with in-house data engineering team to conduct exploratory data analysis to understand data characteristics, identify data quality issues, and address missing or inconsistent data. Implement data cleansing techniques and develop strategies to improve data quality and integrity.
Collaborative Approach: Collaborate with data engineers, software developers, data analysts, and business stakeholders, to integrate data science solutions into existing systems and processes. Provide guidance and support to team members on best practices in data science methodologies and tools.