Opportunity Description
We are seeking a Principal Data Scientist with strongly developed expertise in graph-based AI and related technologies to maximize utilization of data and knowledge in data and digital transformation.
ESSENTIAL EXPERIENCE:
- Lead - Master’s degree in mathematics, computer science, engineering, physics, statistics, economics, computational sciences or a related quantitative discipline and 5+ years of analytical experience in an industrial or commercial setting OR PhD degree with at least 3+ years of industry experience
- DS- Master’s degree in mathematics, computer science, engineering, physics, statistics, economics, computational sciences or a related quantitative discipline and 3+ years of analytical experience in an industrial or commercial setting OR PhD degree with industry experience
- Experience AI/ML/NLP modelling of complex datasets.
- Advanced software development skills in at least two of the standard data
science languages (such as Python, R, Scala, C++, Julia) and strong data
manipulations skills (e.g. SQL, NoSQL, graph, etc.) - Knowledge of SQL and relational databases, query authoring (SQL) and designing variety of databases (e.g. Postgres SQL)
- Comfortable working in cloud and high-performance compute environments (e.g. AWS, Apache Spark) Disciplined AI/ML deployment (MLOps, CI/CD) and Agile delivery
- Experience in coordination of delivery teams and providing feedback to their management
- Excellent written and verbal communication, business analysis, and consultancy skills
Additional required deep experience in one of these areas:
- Graph- Experience AI/ML modelling of complex datasets, network analysis or
direct experience in creating and maintaining graph data models. Experience
with a variety of graph technologies Knowledge of graph databases like Neo4J (Cypher, causal clusters), JanusGraph (Gremlin, GraphML), AWS Neptune, OrientDB. xpertise in machine learning/deep learning-based graph algorithms relevant to link prediction, ranking/recommendation, completion, community detection, node embedding, etc. - Familiarity with Deep Learning, neural network architectures including CNNs,
RNNs, Embeddings, Transfer Learning, Attention-based Networks, Statistical
Learning, Restricted Boltzmann Machines (RBMs), Belief Networks, and
Reinforcement Learning - Deep experience in developing models for private sector or industrial setting e.g.
Requirements
Lead Data Scientist Responsibilities:
- Lead data science area deliverables for digital products / programs / initiatives including the allocation of work within the team, monitors the quantitative and qualitative achievements of the team, and reports results
- Work as an individual contributor, providing data science expertise to digital
products / programs / initiatives. - Apply in-depth experience with both statistical and modern data science
approaches, including unsupervised, supervised, regression algorithms. - Apply advanced techniques such as neural networks, deep learning, NLP and federated learning.
- Build models, algorithms, simulations and experiments by writing highly
optimized code and using state-of-the art machine learning technologies. - Collaborate cross-functionally in teams involved in data driven analytics to
maximize impact of graph-based capabilities - Build and manage support models incorporated into digital or AI products; Work with Infrastructure and Ops teams to ensure appropriate architecture and tooling
- Capacity to mentor junior personnel
- Be able to apply in-depth experience with both statistical and modern data
science approaches to business cases and knowledge management tasks - Strong written and verbal communication skills - ability to communicate complex ideas up to people of varying technical skills
- Work with developers, engineers, and MLOps to deliver AI/ML solutions for new products/services