Senior AI Strategist

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• Collaborate with business stakeholders to understand processes, business problems, challenges, and objectives.
• Identify opportunities to leverage AI and domain expertise to drive efficiency, improve decision-making, and enhance customer experience.
• Align stakeholder interests with the domain team's strategic objectives.
• Collaborate with a cross-functional team of data scientists, engineers, and subject matter experts to create and develop innovative AI solutions.
• Establish a wide network of contacts across the organization to solicit information and proactively gather key information.
• Lead experimentation and prototyping; drive technology development for AI research, experimentation, and prototype development.
• Bring new emerging AI tech into relevant prototypes for the company.
• Facilitate the data science lifecycle from data collection to model deployment in collaboration with stakeholders and partners.
• Identify, collect, and curate relevant data from diverse sources (open-source, government, internal infrastructure); ensure data quality, privacy, and security compliance.
• Manage and oversee AI projects within your domain, ensuring timely delivery, quality standards, and alignment with business objectives.
• Create and maintain roadmaps to provide a high-level framework for strategy, timing, and future work.
• Foster a culture of innovation and knowledge sharing within the AI team; share insights and technical concepts effectively with technical and non-technical audiences.
• Ensure the success of the domain through project management and stakeholder management.
• Stay up-to-date on AI research and development; communicate findings effectively to both technical and non-technical audiences.
• Serve as a subject matter expert in AI, providing guidance, consultation, and collaboration across the bank.
• Collaborate with academic partners; publication contributor: lead or co-author at least one peer-reviewed publication per year.
• Drive initiatives to promote the adoption of graph analytics across the bank, providing guidance, training, and support to various departments.

• Minimum 3+ years of experience developing AI projects in an industry setting.
• Minimum of 2+ years of experience working with graph use cases.
• Proficiency in Python for data science tasks; preferably experience with Jupyter notebooks.
• Familiarity with at least one data science development platform (e.g., Databricks, Cloudera or similar).
• Familiarity with machine learning frameworks (TensorFlow, PyTorch, scikit-learn).
• Ability to manage and provision cloud services on AWS, Azure, or GCP for compute and data products.
• Proficiency in SQL for relational databases; experience with NoSQL databases.
• Data visualization tools such as Matplotlib, Seaborn, or Plotly; basic familiarity with dashboards (PowerBI or Tableau).
• Proficiency in version control systems (Git).
• Familiarity with graph databases and frameworks (e.g., Neo4j, TigerGraph); familiarity with graph analytics tools and techniques.
• Ability to create proofs of concept and minimum viable products, including setting up necessary infrastructure and application design.
• Strong communication and collaboration skills to explain graph analytics insights to non-technical teams.
• Deep understanding of AI concepts (machine learning, deep learning, neural networks); experience with A/B testing to evaluate model performance.
• Experience developing MVP applications to iterate quickly and gather user feedback.
• Understanding of basic database principles and data storage, retrieval, and query performance.
• Ability to optimize system architecture and performance for scalable AI solutions.
• Ability to automate repetitive tasks with scripting; proficient in web scraping for data collection and training.
• Demonstrated willingness to learn new technologies and tools; strong problem-solving and analytical skills.
• Ability to work independently with high quality and productivity; able to learn and integrate new features into existing systems.
• Deep understanding of graph structures, traversal techniques, shortest paths, centrality measures, and clustering.
• Hands-on experience with graph neural networks, graph embeddings (Node2Vec, DeepWalk), PyTorch Geometrics, DGL (Deep Graph Library).
• Strong knowledge of machine learning, deep learning, and statistical modeling to integrate graph analytics into AI workflows.
• Understanding how entities and relationships in graphs influence AI predictions; applied graph analytics to cybersecurity, finance, and social networks.
• Ability to develop APIs and integrate graph analytics into AI pipelines.

• Mid-Senior level position
• Full-time employment

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