Data Scientist

we are looking for Data Scientist (AI) for our client in Dubai, please reach at sajeed.m@lancesoft.com

Data Scientist (AI)

Location Remote -Offshore India

Skill Set Data Science, AI/ML, Banking Domain, Cloud

Duration 6 Months

Shift Hours General

1 Job Title

Data Scientist (AI) Department Direct Supervisor

2 Job Purpose

We are looking for a hands-on Data Scientist with strong technical expertise and a passion for building AI-driven solutions that create real business value. In this role, you will be at the forefront of transforming business problems into scalable AI/ML models using tools such as Python, TensorFlow, PyTorch, Hugging Face, Scikit-learn, and Spark. The ideal candidate will work with structured and unstructured data, leveraging cloud platforms (e.g., Vertex AI, or Azure ML) and integrating models into production environments using MLOps frameworks like MLflow, Kubeflow, and Airflow.

He will collaborate with cross-functional teams including product managers, data engineers, and domain experts to identify opportunities for AI, rapidly prototype models, and deploy solutions on a scale. A strong grasp of NLP, large language models (LLMs), and generative AI is a big plus, especially for use cases involving customer experience, automation, and intelligent decision systems.

This role blends data science rigor with real-world application—perfect for someone who thrives at the intersection of innovation, technology, and business impact.

3 Dimensions

Operating Budget Number of Staff

Capital Exp. Budget Other

4 Key Result Areas

• Collaborate with business units to identify high-impact AI use cases.

• Design and build scalable machine learning models and AI solutions.

• Perform data extraction, preprocessing, feature engineering, and model validation.

• Evaluate model performance and ensure robustness, fairness, and explainability.

• Work with MLOps teams to deploy models into production environments.

• Monitor and retrain models as needed to ensure continued effectiveness.

• Translate complex findings into actionable insights and communicate clearly to non-technical stakeholders.

• Stay current with the latest research and best practices in AI/ML.

• Oversee production of the processes and outputs to meet stakeholders’ needs and requirements.

• Assemble large, complex data sets that meet functional / non-functional business requirements.

5 Operating Environment, Framework and Boundaries, Working Relationships

• Able to Effectively Plan & Organize Their Work

• Strong Interpersonal Communication

• Assist others in completion of their tasks to support the group goals.

• Build and maintain cooperative work relationships with others

6 Problem Solving

• Translate complex business challenges into AI/ML solutions by identifying the right algorithms, tools, and frameworks suited for each use case.

• Apply a structured problem-solving approach to break down ambiguous or unstructured problems, leveraging both quantitative and qualitative data.

• Design end-to-end AI pipelines — from data acquisition to model deployment — to solve real-world problems with measurable impact.

• Leverage domain knowledge to prioritize high-impact AI opportunities, ensuring alignment with business goals and ROI.

• Troubleshoot and resolve technical issues related to data quality, model drift, and performance bottlenecks across cloud-based AI environments.

• Implement fail-safes and fallback logic for mission-critical AI models to ensure resilience and uptime.

• Apply Root Cause Analysis (RCA) and diagnostic techniques to uncover insights from model failures or data anomalies.

• Continuously research emerging trends in AI and apply innovative solutions to stay ahead of industry challenges.

7 Decision Making Authority & Responsibility

• Work on the right tasks by ensuring they know their top deliverables.

• Assume personal responsibility for achieving outcomes.

8 Knowledge, Skills, and Experience

Domain Expertise:

• Proven experience in implementing AI solutions within banking or finance sectors.

• Strong understanding of data privacy, compliance, and ethical AI principles.

AI & Machine Learning:

• Proficiency in building and deploying machine learning, natural language processing (NLP), and generative AI/LLM models using libraries such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face.

• Experience with LLMs (Large Language Models) and their application in use cases like chatbots, document summarization, intelligent search, and virtual assistants.

Cloud & Azure Technologies:

• Deep hands-on experience with Azure Machine Learning (AML) for developing, training, and operationalizing ML models.

• Skilled in using Azure Cognitive Services (e.g., Language Understanding, Computer Vision, Speech, and Text Analytics) to create intelligent AI capabilities.

• Familiarity with Azure Databricks for scalable data processing and collaborative ML development.

• Expertise in integrating AI workflows with Azure Data Lake, Azure Synapse Analytics, and Azure Data Factory.

• Working knowledge of the Azure OpenAI Service, including model fine-tuning and deployment.

• Experience with Azure DevOps and CI/CD pipelines for model lifecycle automation.

• Implementation experience of real-time AI solutions using Azure Event Hubs, Azure Stream Analytics, and Azure Functions.

MLOps & Model Lifecycle:

• Practical experience with MLflow, Kubeflow, and Apache Airflow for model orchestration and tracking.

• Familiarity with tools like InterpretML, and Azure Responsible AI dashboards for bias detection, interpretability, and model governance.

• Knowledge of model versioning, monitoring, and lifecycle management using Model Registry and Azure ML’s native tools.