Data Science Manager

Roles and responsibilities

We are looking for a Manager Data Science - Operations to join our Finance department at GDA. This role involves executing AI and data science projects to enhance operational efficiency using advanced analytics and machine learning techniques.

What You Will Do

• Implement various predictive models, such as price elasticity, predictive maintenance, churn prediction, and anomaly detection in parts utilization
• Create KPI metrics and reporting systems using statistical process control and data visualization libraries (e.g., Plotly, Bokeh)
• Generate insights using statistical analysis, hypothesis testing, and machine learning techniques
  

Required Skills To Be Successful

Bachelor's/MSc in Marketing Analytics, Computer Science, Statistics, or a related field

5+ years of experience in customer analytics or data science

Expertise in Python, SQL, Databricks, and customer segmentation, with experience in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch, ARIMA/SARIMA, Prophet)

Experience with MLOps, Git, and data visualization tools (e.g., Matplotlib, Seaborn, Plotly)

About The Team

You will be part of a dynamic team in the Finance department, collaborating closely with marketing, IT, and customer service departments to drive data-driven decision-making and operational efficiency.

What Equips You For The Role

• Hands-on experience with advanced statistics, machine learning, time series analysis and forecasting, regression, classification, clustering, dimensionality reduction, gradient boosting, neural networks, anomaly detection algorithms, hyper-parameter tuning, natural language processing techniques, text classification and sentiment analysis, and topic modeling
• Strong problem-solving and critical thinking skills, ability to communicate technical concepts to non-technical stakeholders, and project management skills
• Ability to work in cross-functional teams
• Proficiency in multivariate regression and ARIMA models for price elasticity analysis
• Expertise in random forests and gradient boosting machines for predictive maintenance
• Strong skills in logistic regression and survival analysis for churn prediction
• Advanced knowledge of isolation forests and autoencoders for anomaly detection
• Experience with statistical process control and data visualization libraries (e.g., Plotly, Bokeh)
• Skilled in hypothesis testing and machine learning techniques for insight generation
• Familiarity with big data technologies (e.g., Spark) for large-scale data processing
• Understanding of operations research techniques for optimization problems

Desired candidate profile

• Hands-on experience with advanced statistics, machine learning, time series analysis and forecasting, regression, classification, clustering, dimensionality reduction, gradient boosting, neural networks, anomaly detection algorithms, hyper-parameter tuning, natural language processing techniques, text classification and sentiment analysis, and topic modeling
• Strong problem-solving and critical thinking skills, ability to communicate technical concepts to non-technical stakeholders, and project management skills
• Ability to work in cross-functional teams
• Proficiency in multivariate regression and ARIMA models for price elasticity analysis
• Expertise in random forests and gradient boosting machines for predictive maintenance
• Strong skills in logistic regression and survival analysis for churn prediction
• Advanced knowledge of isolation forests and autoencoders for anomaly detection
• Experience with statistical process control and data visualization libraries (e.g., Plotly, Bokeh)
• Skilled in hypothesis testing and machine learning techniques for insight generation
• Familiarity with big data technologies (e.g., Spark) for large-scale data processing
• Understanding of operations research techniques for optimization problems