Senior Data Scientist - Machine Learning, Dataiku and Azure Databricks

DiceTek UAE; Dubai; On-site; AED 300,000 - 600,000; Full time

Job summary

Dicetek LLC in Dubai, UAE, is seeking a Senior Data Scientist to develop and deploy advanced ML and AI solutions using Python, Dataiku and Azure Databricks. You will work across the full data science lifecycle, transforming datasets into actionable insights and scaling production models.

The role requires 5+ years of experience, a strong background in NLP, generative AI, MLOps, and distributed data processing, and hands-on use of PySpark, MLflow, Databricks, and ML deployment.

Qualifications

• 5+ years hands-on data science, ML and AI solution development.
• Experience deploying production ML models.
• Strong Python and ML libraries proficiency.

Responsibilities

• Design and develop advanced data science solutions for business needs.
• Build scalable ML models and productionize AI solutions.
• Operate end-to-end ML lifecycle including deployment and monitoring.

Skills

Python

Dataiku

Azure Databricks

Pandas

NumPy

Scikit-learn

XGBoost

TensorFlow

PyTorch

Education

Bachelor's or higher in CS/AI/Data Science

Tools

Dataiku

Azure Databricks

MLflow

Databricks Model Serving

Job description

Senior Data Scientist - Machine Learning, Dataiku and Azure Databricks Job Snapshot - Role: Senior Data Scientist - Location: Dubai, United Arab Emirates - Industry: I T and Services - Function: Information Technology - Experience: 5 years - Job Type: Full Time - Work Arrangement: On-site

Dicetek LLC is seeking an experienced Senior Data Scientist to develop and deploy advanced machine learning, predictive analytics and artificial intelligence solutions within the Data & Artificial Intelligence division in Dubai, United Arab Emirates. The role focuses on transforming complex datasets into actionable business insights, developing scalable machine learning models and operationalizing AI solutions using Python, Dataiku and Azure Databricks. The successful candidate will work across the complete data science lifecycle, including data preparation, exploratory analysis, feature engineering, statistical modeling, model development, validation, deployment, monitoring and performance optimization. The position requires strong experience with Python libraries such as Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow and PyTorch, together with hands‑on expertise in Dataiku, PySpark, MLflow, Azure Machine Learning and Databricks Model Serving. Candidates should have at least five years of practical experience in data science, machine learning, predictive analytics and AI solution development, including deploying and maintaining production‑ready models. The role also involves Natural Language Processing (NLP), Generative AI, large‑scale distributed data processing and MLOps practices to ensure reliable, scalable and measurable business outcomes.

Job Details - Country: United Arab Emirates - City: Dubai - Industry: I T and Services - Function: Information Technology - Salary: Not disclosed - Gender: No Preference - Candidate Nationality: Not specified - Job Type: Full Time - Work Arrangement: On‑site

Key Responsibilities

Data Science and Advanced Analytics - Design, develop and implement advanced data science solutions to address business requirements.

• Analyse structured and unstructured datasets to identify meaningful patterns, trends and relationships.
• Apply statistical modeling and predictive analytics techniques to complex business problems.
• Transform data into actionable insights that support business decision‑making.
• Evaluate analytical approaches based on business objectives and data characteristics.
• Develop scalable and production‑ready data science solutions.
• Support measurable business value through effective application of machine learning and artificial intelligence.
• Maintain analytical accuracy and consistency throughout the data science lifecycle.

Machine Learning Model Development - Develop machine learning models using Python and relevant scientific computing libraries.

• Apply supervised and unsupervised learning techniques to business use cases.
• Build regression and classification models for predictive analysis.
• Develop clustering algorithms to identify patterns and group related observations.
• Implement forecasting models to support predictive business requirements.
• Develop recommendation models for relevant business applications.
• Apply machine learning techniques to customer segmentation.
• Build analytical models supporting risk assessment and fraud detection.
• Evaluate model suitability, predictive performance and practical business relevance.

Python Programming and Machine Learning Frameworks - Develop data science solutions using Python.

• Use Pandas for data manipulation, transformation and analysis.
• Apply NumPy for numerical computing and array‑based operations.
• Develop machine learning workflows using Scikit‑learn.
• Implement gradient boosting models using XGBoost.
• Use TensorFlow and PyTorch for relevant deep learning applications.
• Maintain efficient and reusable machine learning code.
• Optimize analytical workflows to support performance and scalability.

Data Preparation and Feature Engineering - Prepare and transform structured and unstructured data for analytical processing.

• Perform data cleaning and preprocessing to improve dataset quality.
• Develop feature engineering techniques to improve model performance.
• Identify relevant variables and relationships within datasets.
• Apply appropriate data transformation methods for machine learning workflows.
• Support reproducible data preparation and model development processes.
• Validate data inputs and derived features before model training.
• Improve the efficiency of data preparation workflows through automation.

Dataiku Platform Development - Use Dataiku for end‑to‑end data science and machine learning workflows.

• Develop data preparation and feature engineering processes within Dataiku.
• Build and validate machine learning models using Dataiku capabilities.
• Deploy data science solutions through supported platform workflows.
• Automate recurring data preparation and model execution activities.
• Monitor deployed models and related analytical processes.
• Support production management of Dataiku‑based machine learning solutions.
• Optimize Dataiku workflows for reliability and operational efficiency.

Azure Databricks and Distributed Data Processing - Develop large‑scale data science solutions using Azure Databricks.

• Use PySpark for distributed data processing and transformation.
• Analyse large datasets using scalable processing techniques.
• Build machine learning workflows within Databricks environments.
• Integrate data processing and model development activities.
• Support efficient execution of analytical workloads on distributed infrastructure.
• Optimize data processing performance for large‑scale applications.
• Collaborate with relevant teams to support reliable analytical data workflows.

SQL and Large‑Scale Data Analysis - Write and optimise SQL queries for analytical data extraction and transformation.

• Work with large‑scale structured datasets.
• Analyse unstructured data using appropriate processing techniques.
• Combine relevant data sources to support analytical requirements.
• Validate data consistency and completeness for model development.
• Support efficient data retrieval and preparation for machine learning workloads.
• Apply analytical techniques to extract meaningful information from complex datasets.

Statistical Modeling and Predictive Analytics - Apply statistical modeling techniques to support data‑driven analysis.

• Develop predictive models for relevant business scenarios.
• Evaluate model assumptions and statistical relationships.
• Perform model validation using appropriate analytical techniques.
• Apply hyperparameter optimisation to improve predictive performance.
• Assess model generalisation and reliability.
• Use model explainability techniques to interpret predictions and analytical outcomes.
• Communicate relevant model findings and performance characteristics to stakeholders.

Natural Language Processing and Generative AI - Develop Natural Language Processing solutions for text‑based analytical requirements.

• Apply text analytics techniques to extract insights from unstructured information.
• Implement sentiment analysis for relevant business use cases.
• Support the development of Generative AI solutions.
• Evaluate appropriate NLP and AI approaches for specific analytical requirements.
• Integrate language‑based analytical capabilities into relevant data science workflows.
• Validate the performance and suitability of NLP and Generative AI solutions.
• Support the operationalisation of AI solutions within production environments.

Machine Learning Deployment and Model Serving - Deploy machine learning models into production environments.

• Use MLflow for relevant machine learning lifecycle activities.
• Implement model deployment workflows using Databricks Model Serving.
• Support production deployment through Azure Machine Learning.
• Validate deployed models against operational and performance requirements.
• Monitor model execution and prediction performance.
• Troubleshoot issues affecting deployed machine learning solutions.
• Support reliable and scalable model serving capabilities.
• Maintain consistency between model development and production deployment processes.

MLOps and Machine Learning Automation - Implement MLOps practices across the machine learning lifecycle.

• Develop and maintain CI/CD pipelines for machine learning solutions.
• Apply model versioning and experiment tracking practices.
• Use model registries to manage machine learning artefacts.
• Automate model retraining where appropriate.
• Support repeatable and reliable model deployment processes.
• Integrate model monitoring into operational workflows.
• Improve the efficiency of machine learning development and production management.
• Support consistent lifecycle management of deployed AI solutions.

Model Monitoring and Performance Optimization - Monitor production models for accuracy, reliability and performance.

• Identify changes in model behaviour and prediction quality.
• Implement model drift detection practices.
• Analyse model performance against relevant evaluation criteria.
• Investigate performance degradation and operational issues.
• Support model retraining and optimisation activities.
• Apply hyperparameter optimisation and relevant tuning techniques.
• Maintain visibility of model performance throughout the production lifecycle.

Model Explainability and Governance - Apply model explainability techniques to support interpretation of machine learning outcomes.

• Maintain appropriate documentation of model development and validation activities.
• Support model governance requirements throughout the machine learning lifecycle.
• Use experiment tracking and model registries to improve traceability.
• Document relevant model assumptions, evaluation results and deployment information.
• Support consistent management of model versions and lifecycle changes.
• Promote reproducibility and transparency in machine learning development.
• Consider governance requirements when operationalising AI solutions.

Business Value and AI Solution Delivery - Translate analytical requirements into practical data science solutions.

• Identify opportunities to apply machine learning and AI to business challenges.
• Develop solutions that provide actionable insights and measurable business value.
• Evaluate model outputs in relation to intended business outcomes.
• Support the transition of analytical models into production‑ready applications.
• Communicate relevant analytical findings and technical considerations to stakeholders.
• Contribute to the continuous improvement of data science and AI capabilities.

Required Qualifications and Experience

• Bachelor 's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering or a related field.
• Minimum 5 years of hands‑on experience in Data Science, Machine Learning, Predictive Analytics and AI solution development.
• Strong programming expertise in Python.
• Practical experience using Pandas, NumPy and Scikit‑learn.
• Experience with XGBoost, TensorFlow and PyTorch.
• Hands‑on production experience with Dataiku.
• Experience using Dataiku for data preparation, feature engineering, model development, deployment, automation and monitoring.
• Practical experience with Azure Databricks and PySpark.
• Experience using MLflow for machine learning lifecycle management.
• Strong SQL skills and experience working with large‑scale datasets.
• Experience processing structured and unstructured data.
• Deep understanding of machine learning and statistical modelling.
• Strong knowledge of predictive analytics, data mining and feature engineering.
• Experience with model validation, hyperparameter optimisation and model explainability.
• Experience developing regression, classification, clustering and forecasting models.
• Practical knowledge of recommendation systems and customer segmentation.
• Experience developing analytical models for risk assessment and fraud detection.
• Hands‑on experience with NLP, text analytics, sentiment analysis and Generative AI.
• Proven experience deploying and managing production machine learning models.
• Experience with Databricks Model Serving and Azure Machine Learning.
• Knowledge of MLOps practices, including CI/CD, model versioning and experiment tracking.
• Experience with model registries, automated retraining and model monitoring.
• Understanding of drift detection, performance optimisation and model governance.
• Ability to develop scalable and production‑ready data science solutions.

Preferred Qualifications

• Master 's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering or a related field.

Professional Competencies

• Strong analytical and statistical problem‑solving abilities.
• Ability to develop practical solutions to complex data challenges.
• Excellent Python programming and machine learning development skills.
• Strong attention to data quality, model accuracy and analytical reliability.
• Ability to evaluate and optimise machine learning performance.
• Understanding of scalable data processing and production deployment requirements.
• Ability to communicate technical findings and analytical insights effectively.
• Structured approach to experimentation, validation and model monitoring.
• Strong technical troubleshooting and optimisation capabilities.
• Commitment to reproducible, maintainable and production‑ready AI solutions.
• Focus on delivering measurable business value through advanced analytics.
• Ability to work across the complete machine learning lifecycle.

The ideal candidate will be an experienced Senior Data Scientist with at least five years of hands‑on expertise in machine learning, predictive analytics and artificial intelligence solution development. The role requires strong Python programming skills, practical experience with Dataiku and Azure Databricks, and proficiency in developing scalable machine learning models using libraries such as Scikit‑learn, XGBoost, TensorFlow and PyTorch. Candidates should demonstrate expertise in statistical modeling, feature engineering, NLP, Generative AI and production model deployment using MLflow, Databricks Model Serving and Azure Machine Learning. A strong understanding of MLOps, model monitoring, automated retraining, drift detection and model governance will be essential for developing reliable, scalable and business‑focused AI solutions.