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.