Predictive Modeler (The Forecasting Expert)

Are you passionate about using data to predict trends and guide business decisions? Do you excel at building predictive models that unlock future insights and provide a competitive edge? If you’re ready to apply your analytical expertise to shape impactful forecasts, our client has the perfect role for you. We’re seeking a Predictive Modeler (aka The Forecasting Expert) to develop and deploy predictive models that support strategic decision-making, from customer behavior predictions to risk assessment.

As a Predictive Modeler at our client, you’ll collaborate with data scientists, analysts, and business stakeholders to create models that anticipate trends, optimize processes, and mitigate risks. Your work will be essential in turning historical data into valuable forecasts that inform marketing, finance, and operational strategies.

Key Responsibilities:

1. Develop Predictive Models:
• Build and implement models for forecasting, customer behavior prediction, demand forecasting, and risk assessment. You’ll leverage data to predict outcomes that drive informed decision-making across various departments.
2. Conduct Data Exploration and Feature Engineering:
• Analyze datasets to identify trends, patterns, and relevant features. You’ll prepare data for modeling by cleaning, transforming, and selecting variables that enhance model accuracy.
3. Collaborate on Model Deployment and Integration:
• Work closely with data engineers and software developers to deploy predictive models into production. You’ll ensure models are scalable, maintainable, and optimized for real-time applications.
4. Optimize and Maintain Model Performance:
• Continuously monitor model accuracy and refine models based on new data and performance metrics. You’ll implement retraining workflows to maintain reliability over time.
5. Apply Advanced Statistical and Machine Learning Techniques:
• Use techniques such as regression analysis, time-series forecasting, classification, and clustering to solve complex forecasting problems. You’ll stay updated on the latest methods to improve model performance.
6. Create Reports and Visualizations for Insights:
• Develop reports and visualizations that communicate model outputs and forecasts to stakeholders. You’ll ensure that insights are actionable and accessible for non-technical users.
7. Stay Updated on Industry Trends and Modeling Tools:
• Keep current with advancements in predictive modeling, machine learning, and data tools. You’ll bring innovative techniques and tools that improve forecasting accuracy and efficiency.

Requirements

Required Skills:

• Predictive Modeling and Machine Learning Expertise: Proficiency in building predictive models, including experience with regression, classification, time-series analysis, and ensemble methods.
• Programming and Data Analysis: Proficiency in Python, R, or SQL for data analysis and model development, with experience in libraries like Scikit-learn, TensorFlow, or XGBoost.
• Statistical and Mathematical Knowledge: Strong foundation in statistics and mathematics, with the ability to apply these skills to model building and feature selection.
• Data Manipulation and Feature Engineering: Ability to handle and preprocess data, engineer features, and select variables that enhance model performance.
• Communication and Visualization: Strong skills in creating visualizations and reports to communicate complex predictions to stakeholders. Experience with Tableau, Power BI, or Matplotlib is beneficial.

Educational Requirements:

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field. Equivalent experience in predictive modeling or data science may be considered.
• Certifications in machine learning or data science (e.g., AWS Certified Machine Learning – Specialty, TensorFlow Developer Certificate) are advantageous.

Experience Requirements:

• 3+ years of experience in predictive modeling or data science, with hands-on experience applying models to real-world problems.
• Experience deploying models into production environments and monitoring model performance.
• Background in a specific industry, such as finance, healthcare, or marketing analytics, is beneficial but not required.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.