Data Scientist (Applied AI)

RBC•Minneapolis, MN

1d•$80,000 - $140,000•Onsite

About The Position

As a Data Scientist, you will problem solve, analyze, design, implement, and monitor machine learning applications using state-of-the-art tools and algorithms. As part of the US Wealth Management Applied AI team, your day-to-day work will include many exciting fields in applied statistics and machine learning, including causal inference and experimentation, time-series forecasting, predictive marketing and advertising, and model interpretation.

Requirements

Master’s or PhD in Computer Science, Statistics, Mathematics, or Physics
2-3 years of experience in data solutions and applying data techniques.
Strong knowledge of design, development, and implementation experience utilizing data science technologies.
3+ years of experience in machine learning, predictive analytics, and statistical modeling.
Experience with causal inference and experimentation, time-series forecasting, predictive marketing and advertising, and model interpretation.
Experience with libraries such as: ML/DL: sklearn, tensorflow, pytorch; Analysis: pandas, pySpark, scala Spark, SQL, Hive
Excellent analytical, problem solving, time management and organizational skills.
Experience delivering presentations to wide audiences, both technical and non-technical.
Basic understanding of Generative AI and LLMs.
Familiarity with RAG concepts.
Experience using OpenAI, Claude, Gemini, or Azure OpenAI APIs.
Understanding of prompt engineering.
Ability to evaluate LLM outputs.
Awareness of hallucinations and responsible AI.

Nice To Haves

Good understanding of AWS Big Data services such as Amazon S3, EMR and Glue; and ML capabilities including Sagemaker
Experienced with Jupyter Notebooks, IntelliJ, Visual Studio Code other notebook environments
Experience with visualization tools such as Tableau, QuickSight, Looker
Experience working in Unix/Linux environments
Experience working as part of an agile team using Github and JIRA
Experience with digital data collection tools such as Google Analytics
Exposure to Snowflake, Databricks, Sparr, Power BI or Tableau.
MLflow, NLP, embeddings, semantic sea
Feature stores and model monitoring. REST APIs and FastAPI

Responsibilities

Collaborate with key business partners and stakeholders to understand business objectives/opportunities and problem statements in order to provide solutions that align to business needs that are actionable with a tangible outcome
Prepare, analyze, and transform data from structured and unstructured sources
Develop, implement, and deploy statistical and ML models at scale
Leverage visualization tools/packages to story-tell and to convey data-driven insights with actionable recommendations to key stakeholders
Quickly learn new methods, tools and technologies presented in research communities to implement, adapt and innovate
Effectively communicate findings to business partners and executives.
Developing predictive data models, quantitative analyses and visualization of targeted, big data sources.
Assist Senior Data Scientists throughout the ML lifecycle.
Perform offline model evaluation and validation.
Build dashboards and communicate insights.
Monitor production models for drift and performance.
Document experiments and support AI governance.
Present findings to technical and business stakeholders.