Data Science Manager | Al- Futtaim Automotive | Finance

Tanqeeb; Abu Dhabi; On-site; AED 300,000 - 520,000; Full time

Job summary

Tanqeeb is seeking a Manager Data Science – Operations in Dubai to lead AI and analytics projects that boost efficiency and drive data-driven decisions. You will oversee model development, KPI reporting, and insights generation across operations, finance, and customer experience, guiding a team of data scientists.

The role emphasizes hands-on ML, statistical analysis, and effective stakeholder communication to deliver measurable business value.

Qualifications

• Strong expertise in advanced statistics, ML, time series forecasting, regression, classification, clustering, and anomaly detection.
• Proficiency in Python and SQL; experience with Databricks and ML libraries.
• 4–5+ years in customer analytics, finance analytics, operations analytics, or data science.
• Degree in computer science, statistics, data science, or related field.

Responsibilities

• Deliver AI and data science use cases that improve operational performance.
• Build and implement predictive models (price elasticity, predictive maintenance, churn, anomaly detection).
• Develop KPIs and reporting with statistical process control and data visualization.
• Generate actionable insights through statistical analyses and ML techniques.
• Collaborate with cross-functional teams to translate data into practical recommendations.
• Lead data scientists to ensure timely delivery of analytics solutions.

Skills

Statistics & ML

Time series & forecasting

Python

SQL

NLP techniques

Cross-functional collaboration

Stakeholder management

Education

Bachelor's or Master's in CS/Statistics/Data Science

Tools

Databricks

scikit-learn

TensorFlow

PyTorch

ARIMA/SARIMA

Prophet

Plotly/Matplotlib/Seaborn

Spark

Job description

Job description

We are looking for a Manager Data Science – Operations to join our Digital Technology and Customer Experience team in Dubai. In this role, you will lead and execute AI and data science projects that improve operational efficiency, support business decision-making, and deliver measurable value through advanced analytics, machine learning, and data-driven insights.

What you will do

• Deliver AI and data science use cases that improve operational performance across the business.
• Build and implement predictive models including price elasticity, residual value prediction, predictive maintenance, churn prediction, and anomaly detection.
• Develop KPI metrics and reporting systems using statistical process control and data visualization tools.
• Generate actionable insights through statistical analysis, hypothesis testing, and machine learning techniques.
• Work closely with cross-functional teams and business stakeholders to translate complex data into clear, practical recommendations.
• Lead and support data scientists to ensure timely delivery of high-quality analytics solutions.

Skills

Required skills to be successful

• Strong hands‑on experience in advanced statistics, machine learning, time series analysis, forecasting, regression, classification, clustering, and anomaly detection.
• Expertise in Python, SQL, Databricks, and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, ARIMA/SARIMA, and Prophet, Natural Language Processing techniques.
• Minimum 4- 5 years of experience in customer analytics, finance analytics, operations analytics, or data science.
• Bachelor’s or master’s degree in computer science, Statistics, Data Science, or a related field.

About the team

You will be part of the Digital Technology and Customer Experience team, working closely with operations, business, and technology stakeholders. The team focuses on using data, analytics, and AI to solve business challenges, improve operational efficiency, and enable better customer and commercial outcomes.

What equips you for the role

• Proven experience delivering machine learning and advanced analytics solutions in a business environment.
• Strong ability to work in cross-functional teams and manage multiple stakeholders.
• Experience in translating data science outputs into practical business.
• Experience with data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, and Bokeh.
• Good understanding of MLOps, Git, big data technologies such as Spark, and optimization techniques.
• Ability to communicate technical concepts clearly to non-technical stakeholders.
• Strong problem-solving, critical thinking, project management, and stakeholder management skills.