Data Science Lead

What You Will Do

• Design, document, and implement predictive models and machine learning algorithms that demonstrate clear ROI to business stakeholders. Test and refine these models rapidly in response to new data and business needs.
• Lead early-stage discussions on data potential and predictive insights to influence project scopes and deliverables, ensuring alignment with strategic goals.
• Architect and manage robust, scalable data pipelines to support ongoing data ingestion and analysis, optimizing for speed and data integrity.
• Utilize advanced technologies in AI and machine learning, including TensorFlow, PyTorch, and Azure Data and AI services, to enhance analytics capabilities.
• Translate complex business needs into clear technical and functional specifications to ensure precise implementation of data solutions.
• Work collaboratively across functional teams to integrate data insights into business processes and decision-making frameworks, enhancing the collective data competency of the organization.
• Strategic Contribution: Digital Risk Management, Lead the implementation of strategic initiatives and maintain a robust framework using industry standards (NIST, COBIT, ISO 27001) to mitigate cybersecurity threats and safeguard data.
• Compliance and Gap Assessment: Ensure adherence to evolving regulatory requirements and industry standards (ADHICS, NESA, PCI-DSS, ISO 27001, ISO 27701, ISO 22301, ISO 28000, SWIFT KYC), minimizing compliance risks.
• Vendor Risk Management: Develop and implement a comprehensive strategy to manage vendor-related risks aligned with the organization's risk appetite and business objectives.
  

Required Skills To Be Successful

• Machine Learning Development and Prototyping: Pulling datasets from SQL, Serializing ML models
• Data Analysis and Experimentation: Conducting inferential analyses and data investigations
• Communication and Collaboration: Clearly communicating ML/algorithm designs to cross-functional teams.
• Technical Proficiency:Proficiency in scripting languages (SQL, Python, Spark)
• Experience with Azure tools (Databricks, Azure ML, etc.) and developer tools (Azure DevOps/GitHub, Docker)
• Experience in MLOps and ML Frameworks:Proficiency in MLOps and LLMOps practices
• Experience with ML libraries and frameworks (TensorFlow, PyTorch, MLFlow, OpenAI, LangChain)
  

What Equips You For The Role

• 15+ years of working experience in data science, with at least 5 years working in developing and deployment ML/DL solutions focused on batch and real-time data pipelines and 3+ years working as Data Science Lead
• Proven record of delivering business impact through analytics solutions
• Experience with probability and statistics inclusive of machine learning, optimization, forecasting and experimental design.