Data Scientist

Responsibilities

• Process, cleanse, and verify the integrity, quality, and reliability of data used for analysis, reporting, and predictive modeling.
• Perform ad-hoc and recurring analyses, presenting results and insights in a clear, concise manner to support business decision-making.
• Analyze large, complex datasets to extract meaningful insights, selecting appropriate statistical, analytical, and machine learning techniques based on the problem context.
• Select relevant features and build, evaluate, and optimize classification and predictive models using machine learning techniques.
• Apply data mining and advanced analytical methods to identify trends, patterns, and relationships within structured and unstructured data.
• Extend and enrich customer and business datasets using third-party data sources when required to improve analytical outcomes.
• Enhance data collection and preparation procedures to ensure relevant, high-quality inputs for analytic and machine learning systems.
• Use data modeling and evaluation strategies to identify patterns and accurately predict unseen or future instances.
• Collaborate with business and technical stakeholders to align analytical solutions with business objectives.
• Adhere to the Underwriters Laboratories Code of Conduct and follow all physical and digital security, data governance, and compliance practices.
• Collaborate with business stakeholders to gather, analyze, and document requirements, translating needs into BRDs, user stories, and functional specifications.
• Analyze large, complex datasets using statistical and analytical techniques to identify trends, patterns, and actionable insights.
• Build and maintain dashboards, reports, and data visualizations using Power BI, Tableau, or similar tools.
• Apply statistical methods and basic machine learning models to support forecasting, prediction, and business decision-making.
• Perform ad-hoc and recurring analyses to support both operational reporting and strategic initiatives.
• Process, cleanse, validate, and maintain data accuracy and integrity across analytical datasets.
• Enhance data collection processes and integrate third-party data sources to improve analytical coverage and model performance.
• Communicate analytical findings and recommendations clearly to both technical and non-technical audiences.
• Partner with development teams using Azure DevOps to manage work items, track progress, and ensure timely delivery.
• Support data science and analytics initiatives using SQL, Python, R, and Excel.
• Collaborate with data engineering teams to support analytics pipelines and data lake initiatives.

Qualifications

• Master’s degree in Data Science, Data Analytics, Computer Science, or related field
• 3–5 years of experience in data analytics or data science roles
• Strong proficiency in SQL, Python, and R
• Experience with Power BI, Tableau, or similar visualization tools, including data modeling and DAX
• Strong analytical and problem-solving skills with attention to detail
• Experience gathering and documenting business requirements
• Familiarity with Agile methodologies and Azure DevOps
• Excellent communication, interpersonal, and stakeholder management skills
• Understanding of statistical methods and probability theory

Preferred

• Experience with machine learning frameworks such as scikit-learn, TensorFlow, or Keras
• Experience with Salesforce data and reporting
• Experience in business services or manufacturing industries
• Familiarity with NLP or deep learning techniques

Benefits

• Estimated salary range: $80,000 to $120,000
• Annual bonus compensation with a target payout of 10% of the base salary
• Health benefits: medical, dental, and vision
• Wellness benefits: mental and financial health
• Retirement savings (401K) commensurate with the standard rewards offered in each individual location or country
• Paid time off: 15 days of vacation, 12 days of holiday including floating holidays, and 72 hours of sick time off