Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global technology organization is seeking a Data Science Engineer to support its Finance team in developing advanced predictive techniques for forecasting customer retention metrics, units, and annual recurring revenue (ARR). The role combines data science, financial analysis, automation, and artificial intelligence to improve forecasting processes and deliver actionable insights.
The position offers significant exposure to cross-functional teams and senior leadership. The successful candidate will work across the full data science lifecycle, including data preparation, statistical analysis, predictive modeling, automation, visualization, and communicating insights to business stakeholders.
The role will focus on developing and enhancing forecasting processes by applying automation and AI/ML techniques. The Data Science Engineer will partner with finance and business stakeholders to understand business requirements, refine existing forecasting methodologies, and develop innovative solutions to improve accuracy and efficiency.
The successful candidate will also have opportunities to present findings and recommendations to senior leadership while contributing to initiatives that modernize financial analysis and forecasting.
Key Responsibilities
- Drive data science modeling initiatives for forecasting and business analysis.
- Automate, enhance, and maintain analytical applications and data structures used for modeling and forecasting.
- Monitor weekly performance, identify root causes of changes in key metrics, and continuously improve forecasting algorithms.
- Partner with data visualization teams to develop and maintain dashboards that track important business indicators.
- Collaborate with Finance and cross-functional teams to develop data science models that address a broad range of business challenges.
- Analyze business and financial data to identify opportunities for process and system automation.
- Analyze data to identify and resolve operational issues.
- Reverse engineer existing data flows and source-to-target mappings and collaborate with data engineering teams to implement improvements.
- Prepare analytical reports documenting financial models, methodologies, and data processes.
- Demonstrate intellectual curiosity and continuously improve existing methodologies and processes.
- Capture, synthesize, and interpret disparate quantitative data within the context of business objectives.
- Identify trends and explore data through segments and cohorts.
- Communicate actionable insights and recommendations to finance and business leadership.
- Support the adoption of new and innovative technologies across the organization.
- Serve as a change agent by helping users adopt improved analytical processes and technologies.
Required Qualifications
- Strong proficiency in SQL and Python; experience with R is a plus.
- Experience working with cloud-based data platforms such as Databricks, AWS, or Snowflake.
- Understanding of machine learning techniques, particularly supervised learning applied to time-series analysis.
- Proficiency with data visualization tools such as Power BI or Tableau.
- Experience analyzing large datasets, extracting meaningful insights, and translating findings into real-world business outcomes.
- Strong analytical, creative thinking, and problem-solving abilities.
- Demonstrated ability to derive and communicate actionable insights from analytical projects to business or product leaders.
- Degree in a quantitative field such as statistics, economics, applied mathematics, operations research, or engineering, or equivalent relevant work experience.
Preferred Qualifications
- Knowledge of Microsoft Office, particularly Power Query and M/DAX.
- Understanding of version control frameworks such as GitHub.
- Experience with customer cancellation forecasting and cohort retention analysis.
- Experience working with financial planning and analysis processes.
- Experience developing predictive models for business forecasting.
- Experience collaborating with data engineering, finance, and business teams.
- Strong communication and presentation skills, including the ability to present analytical findings to senior leadership.
Disclaimer
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