SIA has multiple positions for machine learning/deep learning experts to drive our AI and data science initiatives.
• Member of the in-house AI Center-of-Excellence team that works on challenging problems in machine learning (including areas on NLP, computer vision, recommender system, transfer learning and reinforcement learning), mathematical optimization, game theory, and experimental design.
• Research, customize when necessary, and develop statistical, machine learning and deep learning algorithms to meet complex product requirements. The scope includes defining hypotheses, executing necessary tests and experiments; evaluate, tuning and optimizing algorithms and methods; and having an eye towards cloud implementation ease, scalability, and robustness in a live customer facing production environment.
• Provide technical direction and guidance to a small and highly skilled team of junior and senior data scientists embedded in Kanban data squads that deliver products/services in AI, data science and data analytics to stakeholders in a large number of business units and serve as go-to expert in your area of ML/DL expertise.
• Work closely with business stakeholders to create impactful, intelligent features and products and collaborate with other team members including other Data Scientists, Engineers and Strategists and “own” the end-to-end process
• Administer and maintain performant cloud and on-premises GPU compute resources for training large ML models in development, and for providing inference services in production.
• PhD degree related to computer science, advanced machine learning or other AI disciplines is required. Consideration will be given to exceptional candidates without advanced degrees.
• Advanced programming skills in Python and/or a modern functional/object-oriented programming language like Scala or Java. Strong skills in algorithm design & analysis, data structure and SQL.
• At least 2 years of relevant industry experience in two or more of the following areas:
- Expert-level hands-on skills in shallow and deep machine learning. Highly conversant with GPU-accelerated deep learning frameworks (such as TensorFlow and PyTorch).
- Demonstrated ability in rapidly adapting, training and deploying state-of-the-art AI models in production based on the latest published research papers and code.
- Knowledge and working experience in workflow, map-reduce or stream processing systems such as Spark and Kafka.
- Strong skills in Bayesian statistics and inference. Comfortable with the application of Bayesian and causal networks for probabilistic reasoning.
• Hands-on experience with AWS, GCP or similar public cloud environment.
• Excellent mentoring, interpersonal and communication skills for working with both technical staff and non-technical business users.
• Demonstrated intellectual firepower to be a rapid problem-solver.
• Experience with Agile/Scrum/Kanban methodologies is a plus.