Data Scientist (Finance & Accounting / Forecasting)

Description

• Communicate analytical findings in clear, actionable terms for non-technical audiences
• Design scalable dashboards and analytical tools for central use
• Explore internal datasets to extract meaningful business insights and communicate results effectively and write efficient, reusable code for data improvement, manipulation, and analysis
• Mine and analyze data to improve forecasting accuracy, optimize marketing techniques, and informed business strategies, developing and managing tools and processes for monitoring model performance and data accuracy
• Explain complex models (e.g., RandomForest, XGBoost, Prophet, SARIMA) in an accessible way to stakeholders
• Manage project codebase using Git or equivalent version control systems
• Work cross-functionally to implement and evaluate model outcomes
• Apply statistical techniques (regression, distribution analysis, hypothesis testing) to derive insights from data and create advanced algorithms and statistical models such simulation, scenario analysis, and clustering
• Enable quick prototype creation for analytical solutions and develop predictive models and machine learning algorithms to analyze large datasets and identify trends
• Visualize and present data using tools such as Power BI, ggplot, and matplotlib
• Build strong collaborative relationships with stakeholders across departments to drive data-informed decision-making while also helping in the identification of opportunities for leveraging data to generate business insights

Requirements

• Proficiency in machine learning libraries (statsmodels, prophet, mlflow, scikit-learn, pyspark.ml)
• Strong communication and collaboration skills, with the ability to explain complex concepts to non-technical audiences
• 3+ years of relevant experience in data science and analytics and adept in building and deploying time series models
• Competence in SQL and relational databases along with experience using visualization tools such as Power BI
• Experience with big data frameworks such as PySpark along strong knowledge of data cleaning packages (pandas, numpy)
• Proficiency with version control systems such as BitBucket and Python programming
• Knowledge of statistical and data mining techniques such as GLM/regression, random forests, boosting, and text mining
• Familiarity with project management tools such as Jira along with experience in cloud platforms and services such as DataBricks or AWS
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related technical field

Benefits

• Access to training and development programs to enhance your payroll expertise
• Career advancement with an established framework is in place – clearly defining expectations and outlining opportunities for advancement
• Opportunity to grow within a supportive and collaborative team environment

Training + Development

Interview process

Visa Sponsorship

Security clearance