Senior Data Scientist

Overview:

We are seeking a Senior Data Scientist to lead the development and implementation of advanced predictive models aimed at improving sales forecasting and demand planning as part of Hendrick’s product offering. In this role, you'll analyze complex data, leverage state-of-the-art machine learning to forecast sales, optimize inventory management, and inform decision-making on pricing, promotions, and staffing. This position is ideal for candidates who have deep technical expertise in data science, strong analytical thinking, and a proven track record of successfully driving business outcomes in retail and QSR (Quick Service Restaurant) environments.

Key Responsibilities:

1. AI Solution Development: Lead the design, development, and implementation of AI-powered solutions, including machine learning models (Forecasting, Time Series, Regression), deep learning algorithms, and natural language processing systems.
2. Sales Forecasting & Demand Prediction: Build and refine predictive models to project sales figures, customer demand, and seasonality trends for various products and services.
3. Data Integration & Management: Collect, clean, and organize large datasets from diverse sources, including transaction data, customer behavior data, weather patterns, promotions, and market trends.
4. Machine Learning Model Development: Develop and optimize machine learning models such as regression models, time series models (e.g., ARIMA, Prophet), and ensemble methods (e.g., Random Forest, XGBoost) to improve the accuracy of sales forecasts.
5. Pricing & Promotions Optimization: Work closely with marketing and finance teams to simulate the effects of pricing changes and promotions on sales, helping to identify the most profitable strategies.
6. Inventory & Supply Chain Analytics: Use predictive analytics to optimize stock levels, ensuring that inventory meets anticipated demand without overstocking or understocking.
7. Customer Behaviour Analysis: Analyze customer purchase patterns to identify trends that impact demand and integrate this into sales forecasting models.
8. Collaborate with Cross-functional Teams: Partner with marketing, finance, operations, and product teams to drive data-informed business decisions.
9. Advanced Statistical Analysis: Use statistical techniques (e.g., A/B testing, hypothesis testing) to assess the performance of marketing campaigns and other business changes.
10. Continuous Improvement: Monitor and evaluate the performance of predictive models and regularly update them to reflect market conditions, emerging trends, and new data.
11. Model Training and Evaluation: Train, validate, and fine-tune machine learning models using large datasets, ensuring accuracy, robustness, and scalability of AI solutions.
12. Data Preprocessing and Feature Engineering: Preprocess raw data, perform feature extraction, and engineer features to prepare datasets for machine learning tasks.
13. Performance Optimization: Optimize the performance of machine learning models and algorithms to meet performance requirements and constraints.

Required Qualifications:

• Education: Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• Experience: 3-5+ years of experience in data science, with strong experience in sales forecasting and demand planning.
• Technical Skills:
• Proficiency in programming languages such as Python, R, SQL.
• Strong experience with machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Expertise in time series analysis, regression models, and statistical forecasting techniques.
• Experience working with large datasets and big data tools (e.g., Hadoop, Spark, AWS, Google Cloud).
• Data Visualization: Expertise in data visualization tools (e.g., Tableau, Power BI, or similar) to present findings to stakeholders.
• Soft Skills:
• Strong problem-solving skills with a passion for innovation.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts to non-technical audiences.
• Detail-oriented with a commitment to accuracy in all aspects of work.

Preferred Qualifications:

• Familiarity with marketing analytics, pricing algorithms, and consumer insights.
• Experience in cloud-based data engineering and analytics platforms (AWS, Azure, or GCP).

Benefits:

• Competitive salary and performance-based bonuses.
• Health insurance.
• Opportunities for professional growth and continuous learning.

If you have experience with Retail or QSR predictive models (e.g., foot traffic prediction, menu item demand forecasting), please highlight it.