Senior Analyst, Analytics & Insights

Bold Orange Company•Minneapolis, MN

22h

About The Position

Bold Orange (BOCO)’s growing Analytics & Insights team is seeking a Senior Analyst with at least 5+ years of hands-on experience analyzing marketing & customer datasets and experience building machine learning models and/or statistical experiments. A Senior Analyst in the team will be expected to lead the hands-on analytics work end-to-end in these projects, with constant support and mentorship from a Director. This end-to-end work can include identifying what datasets are worth analyzing, provisioning & cleaning data, data exploration & modelling (clustering, forecasting, driver models, etc.), developing actionable insights, designing compelling data visualizations, and presenting / discussing recommendations with clients. Across BOCO Analytics projects, someone in the Senior Analyst role will need to leverage their previous experience and bring a curious, creative energy for ongoing learning. With analytics work spanning customer transactions data, search/AI & web analytics data, email & SMS engagement data, media investment & engagement data, product & in-store reviews data, and more, our team needs to bring fundamental analytics skills and an agile mentality for exploring new domains. The ideal candidate has at least 5+ years of hands-on marketing analytics experience including knowledge of both SQL and Python, comfort with Snowflake and/or other cloud data platforms, and demonstrated experience organizing, integrating, analyzing, and interpreting data to form a clear, compelling story. Experience with digital analytics tools (Google Analytics, digital ads platforms, ESPs, etc.) is not required but preferred.

Requirements

At least 5+ years domain experience within digital analytics, marketing analytics or related field.
Working knowledge of SQL and Python (incl. Pandas) for analytics and machine learning
Experience with statistical modeling and experimental design, including randomized control designs, regression modeling, decision tree / random forest classification, or other machine learning approaches.
Bachelor’s degree in data science, business analytics, social sciences, or another related discipline with meaningful exposure to data analysis.
Ability to thrive in a highly collaborative, cross-functional environment where analytics supports and works closely with tech, creative, and execution teams
Innately curious and keen to learn new skills and new tools
Excellent communication skills – verbal and written. This is a client-facing analytics role.

Nice To Haves

Experience with machine learning & modeling tools including Scikit-Learn, XGBoost, PyMC, Statsmodels, Prophet, and/or Dataiku
Experience with cloud data platforms including Snowflake, Databricks, AWS, Google Cloud, etc. Snowflake experience is especially valuable.
Experience with data analysis & visualization tools including Data Studio (formerly Looker Studio), Tableau, Sigma, PowerBI, and/or Datorama.
Experience with digital experience analytics tools including Google Analytics, Google Ads, Meta Ads, TikTok Ads, and/or SEMRush.
Experience using AI tools (including custom skills / agents) to accelerate data analysis, code generation, or insight development.

Responsibilities

Analyze integrated marketing and customer journey datasets to understand the drivers of success/failure in the context of client strategy and develop clear recommendations for a non-technical audience from this data.
Build machine learning models (scikit-learn, XGBoost) & run statistical experiments (control vs. exposed, MAB) to validate, predict, and identify drivers of marketing outcomes.
Explore and learn to leverage new analytics datasets and tools, helping clients understand how they can support insights and optimization.
Design, build, and train clients to understand reporting, dashboards, and performance insights for ongoing client delivery (Data Studio, PowerBI, Tableau, PowerPoint).
Present and discuss analysis with internal stakeholders and clients, including adapting content for different audiences.
Build and maintain data architecture documentation to ensure clarity across teams on what datasets are canonical and where they come from.
Mentor junior analytics team members to support their ongoing development, always promoting creativity as well as structured thinking.