Associate Data Scientist

Ameriprise Financial Services, LLC•Minneapolis, MN

5d•$71,300 - $96,000

About The Position

Perform exploratory data analysis to identify trends, patterns, and anomalies for tier 0 applications/products. Develop and implement statistical models, machine learning algorithms, and data mining techniques for Service Management initiatives. Key Responsibilities Collect, clean, and preprocess structured and unstructured data from various sources. Develop and implement statistical models, machine learning algorithms, and data mining techniques. Perform exploratory data analysis to identify trends, patterns, and anomalies. Communicate findings through visualizations, dashboards, and reports. Collaborate with engineering, product, and business teams to deploy models and integrate data solutions. Monitor model performance and continuously improve accuracy and efficiency. Stay current with industry trends, tools, and best practices in data science and analytics. LI_DNI About Our Company We’re a diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of 2024. With our team of more than 20,000 people in 20 countries, we advise, manage and protect assets and income of more than 3.5 million individual, small business and institutional clients. We are a longstanding leader in financial planning and advice, a global asset manager and an insurer. Our unwavering focus on our clients and strong financial foundation connects each of our unique businesses - Ameriprise Financial, Columbia Threadneedle Investments and RiverSource Insurance and Annuities. Here, we foster meaningful careers, invest in the future, and make a difference for clients, institutions and communities around the world.

Requirements

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
0-1 years of experience.
Proficiency in programming languages such as Python or R.
Strong knowledge of SQL and data manipulation tools.
Understanding of data engineering concepts: ETL/ELT, data quality, logging, monitoring.
Exposure to AWS CloudWatch, S3, Lambda, EC2.
Understanding of infrastructure concepts such as compute, storage, networking, IAM.
Familiarity with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).
Excellent problem-solving and communication skills.

Nice To Haves

Exposure to Snowflake and Iceberg.
Comfortable with model validation, monitoring, and basic MLOps concepts.
Ability to write scripts to automate reporting or operational tasks
Experience with API integrations (REST, boto3 for AWS, etc.).
Basic understanding of containerization (Docker) is a plus.

Responsibilities

Collect, clean, and preprocess structured and unstructured data from various sources.
Develop and implement statistical models, machine learning algorithms, and data mining techniques.
Perform exploratory data analysis to identify trends, patterns, and anomalies.
Communicate findings through visualizations, dashboards, and reports.
Collaborate with engineering, product, and business teams to deploy models and integrate data solutions.
Monitor model performance and continuously improve accuracy and efficiency.
Stay current with industry trends, tools, and best practices in data science and analytics.