Senior Data Scientist II – Finance Data Science

HubSpot

Senior Data Scientist at HubSpot improving finance operations with data science and ML solutions. Collaborating with teams to develop scalable models and actionable insights.

Posted 6/15/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $153,800 - $246,100 per yearWebsite

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Hard Skills

machine learningnatural language processingdata analysistime series forecastingPythonSQLML model monitoringlarge language modelsretrieval-augmented generationdata extraction

Soft Skills

communicationmentoringcollaborationproblem-solvingdata-driven decision-makingproject leadership

Tools & Technologies

PandasNumPySciPyscikit-learnGit

Certifications & Qualifications

Bachelor's degree in Data ScienceBachelor's degree in Computer ScienceBachelor's degree in StatisticsBachelor's degree in Operations ResearchMaster's degree in related field

Industry Keywords

risk assessmentforecastinganomaly detectioncustomer clusteringfinance operationsdata science best practices

Tech Stack

Tools & technologies

NumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Own end-to-end scalable machine learning models for risk assessment, forecasting, anomaly detection, customer clustering, natural language processing and other key finance initiatives.
Apply large language models (LLMs) to create Gen-AI applications leveraging such as chatbots and intelligent agents to support & streamline finance operations.
Partner with the strategic finance teams to enhance their financial forecasting methods and support data-driven decision-making.
Analyze complex datasets of structured & unstructured data using advanced analytics to generate actionable insights; communicate findings through impactful reports and presentations.
Establish best practices for data science, opportunity to mentor junior analysts and contribute to the team's professional growth.
Collaborate with stakeholders, data scientists and MLEs across various business groups to develop impactful solutions.

Requirements

What you’ll need

Bachelor's degree in Data Science, Computer Science, Statistics, Operations Research, or related field required. Masters preferred.
4+ years of experience as a Data Scientist or ML Engineer in a fast-paced environment owning end-to-end model lifecycle.
Demonstrated experience in solving time series forecasting problems using a variety of techniques, along with delivering a broad range of other ML/AI solutions to address diverse business challenges.
Expert-level proficiency in Python, with hands-on experience using key libraries such as Pandas, NumPy, SciPy, scikit-learn & popular NLP libraries along with strong expertise in version control tools like Git.
Skilled in ML model monitoring, including tracking performance, detecting drifts and ensuring continuous improvement post-deployment.
Experience leveraging large language models (LLMs) and experience with retrieval-augmented generation (RAG) concepts.
Strong proficiency in SQL for data extraction and transformation.
Ability to clearly communicate complex technical concepts to business leaders through presentations and written memos.
Experience leading complex projects and collaborating across functional teams.

Benefits

Comp & perks

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