Principal, Data Science and Advance Analytics

10+ years of experience of working in Data Science or related role in business / consulting setting

Hands on technical expertise, with experience in driving analytics solution end-to-end until production

8+ years in applied Machine Learning, NLP, deep learning, reinforcement learning, supervised and unsupervised learning algorithms.

Solid Understanding and implementation exposure (corporate / learning) generative / GenAI models, Agentic AI frameworks

Strong programming skills in Python, with experience using relevant libraries (TensorFlow, PyTorch, scikit-learn, etc.)

Proficiency in SQL, Statistics, data pipelines and data story telling through visualization methods.

Working knowledge of cloud based and local data science frameworks and toolkits. Experience in Azure / Databricks / Azure Health is preferred but GCP / AWS would be considered.

Ability to build quick prototype Utilizing modern front-end frameworks (e.g., React, Angular, Vue.js) and back-end technologies (e.g., Python/Flask, Node.js) would be a plus, not mandatory

Exposure to building large scale machine learning systems or product development environment would be preferred but not mandatory.

Demonstrate effective oral and written communication, able to deliver convincing, clear presentations to all levels of stakeholders.

Good knowledge of database management, data reporting and analytics, data visualization for business consumption, understanding of best practices with data structures for analytics and reporting, data manipulation, database and analytics skills to develop insightful measures for actions and decision making in a business setting.

Experience applying data science to clinical trials operations (study enrolment acceleration, trial site selection, country profiling, recruitment curve prediction etc), patient identification or medical domain a plus/preferred, but not mandatory.

Demonstrated curiosity and passion about creative problem solving and data innovation, demonstrated accountability in driving project, demonstrated autonomy in leading stakeholder discussions and delivery.

Job Responsibilities:

Define the data science and analytics strategy and manage the delivery roadmap.

Consultative problem solving with stakeholders - problem identification, potential solutions; lead execution of innovative and high-value analytics visualizations.

Implement sound model development methodology that includes clear model quality objectives and quality measurement methods from the onset. Ensure that machine learning models are appropriately trained and tuned for accuracy and relevance with analytical findings within the team. Ensure SDLC principles are followed, and good quality code (ie. Well documented, reusable, bug-free, etc) is delivered.

Work with a team of Data Scientists and Analysts to manage efficient execution of the strategy and roadmap, using advanced analytics (descriptive / inquisitive / predictive), including hands-on support of all development activities. Develop team members on methodology and advanced analytics approaches with a focus on fostering high morale and motivation.

Stay on top of the latest external methods and approaches, current and emerging industry trends, and advanced methods/best practices; Pull through expertise through critical deliverables and work as a “player-coach”.

Appropriately train and maintain machine learning models for accuracy and relevance with analytical findings. Pull through analytics and business acumen through appropriate selection of methods fit for purpose that reveal customer patterns in the data that may spark additional data analysis or research for deep customer insight.

Productize analytics solutions / Proof of concepts as features on Azure based analytics platform using systems understanding, engineering constraints and system performance needs.

Develop compelling summaries that visualize the data through narrative dashboards to clearly tell a succinct story in easy-to-understand ways; Focus on the key findings and insights in alignment with stakeholders' goals/business needs with proper validation techniques to build confidence in the results.