Senior Data Scientist

About the Role

As a Senior Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow.

Responsibilities

• Prototype Solutions: Develop prototype solutions, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets.
• Exploratory Data Analysis: Work on exploratory data analysis to navigate a dataset and draw broad conclusions based on initial appraisals.
• Optimization Recommendations: Provide optimization recommendations that drive KPIs established by product, marketing, operations, PR teams, and others.
• Collaboration: Interact with engineering teams to ensure that solutions meet customer requirements in terms of functionality, performance, availability, scalability, and reliability.
• Business Collaboration: Work directly with business analysts and data engineers to understand and support their use cases.
• Data-Driven Business Solutions: Collaborate with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Drive Innovation: Drive innovation by exploring new experimentation methods and statistical techniques that could sharpen or speed up our product decision-making processes.
• Cross-Training: Cross-train other team members on technologies being developed, while also continuously learning new technologies from other team members.
• Community Contribution: Contribute to unit activities and community building, participate in conferences, and provide excellence in exercise and best practices.
• Sales & Marketing Support: Support marketing & sales activities, customer meetings, and digital services through direct support for sales opportunities and providing thought leadership & content creation for the service.

Requirements

• Education: BSc, MSc, or PhD in Mathematics, Statistics, Computer Science, Engineering, Operations Research, Econometrics, or related fields.
• Mathematics & Statistics: Strong knowledge of Probability Theory, Statistics, and a deep understanding of the mathematics behind Machine Learning.
• Methodologies: Proficiency with CRISP-ML(Q) or TDSP methodologies for addressing commercial problems through data science solutions.
• Machine Learning Techniques: Hands-on experience with various machine learning techniques, including:
• Regression
• Classification
• Clustering
• Dimensionality reduction
• Programming: Proficiency in Python for developing machine learning models and conducting statistical analyses.
• Data Visualization: Strong understanding of data visualization tools and techniques (e.g., Python libraries such as Matplotlib, Seaborn, Plotly) and the ability to present data effectively.
• SQL Proficiency: Proficiency in SQL for data processing, manipulation, sampling, and reporting.
• Data Challenges: Experience working with imbalanced datasets and applying appropriate techniques.
• Time Series Data: Experience with time series data, including preprocessing, feature engineering, and forecasting.
• Anomaly Detection: Experience with outlier detection and anomaly detection.
• Data Types: Experience working with various data types: text, image, and video data.
• Cloud Platforms: Familiarity with AI/ML cloud implementations (AWS, Azure, GCP) and cloud-based AI/ML services (e.g., Amazon SageMaker, Azure ML).

Domain Experience

• Medical Signals & Images: Experience with analyzing medical signals and images.
• Predictive Models: Expertise in building predictive models for patient outcomes, disease progression, readmissions, and population health risks.
• NLP & Text Mining: Experience extracting insights from clinical notes, medical literature, and patient-reported data using NLP and text mining techniques.
• Survival Analysis: Familiarity with survival or time-to-event analysis.
• Clinical Trials: Expertise in designing and analyzing data from clinical trials or research studies.
• Causal Relationships: Experience identifying causal relationships between treatments and outcomes, such as propensity score matching or instrumental variable techniques.
• Healthcare Regulations: Understanding of healthcare regulations and standards like HIPAA, GDPR (for healthcare data), and FDA regulations for medical devices and AI in healthcare.
• Healthcare Data Security: Expertise in handling sensitive healthcare data in a secure, compliant way, understanding the complexities of patient consent, de-identification, and data sharing.
• Decentralized Data Models: Familiarity with decentralized data models such as federated learning to build models without transferring patient data across institutions.
• Interoperability Standards: Knowledge of interoperability standards such as HL7, SNOMED, FHIR, or DICOM.
• Stakeholder Collaboration: Ability to work with clinicians, researchers, health administrators, and policymakers to understand problems and translate data into actionable healthcare insights.

Good to Have Skills

• MLOps: Experience with MLOps, including integration of machine learning pipelines into production environments, Docker, and containerization/orchestration (e.g., Kubernetes).
• Deep Learning: Experience in deep learning development using TensorFlow or PyTorch libraries.
• Large Language Models: Experience with Large Language Models (LLMs) and Generative AI applications.
• SQL: Advanced SQL proficiency, with experience in MS SQL Server or PostgreSQL.
• Data Engineering: Familiarity with platforms like Databricks and Snowflake for data engineering and analytics.
• Big Data: Experience working with Big Data technologies (e.g., Hadoop, Apache Spark).
• NoSQL: Familiarity with NoSQL databases (e.g., columnar or graph databases like Cassandra, Neo4j).

Business-Related Requirements

• Data Science Solutions: Proven experience in developing data science solutions that drive measurable business impact, with a strong track record of end-to-end project execution.
• Business Problem Translation: Ability to effectively translate business problems into data science problems and create solutions from scratch using machine learning and statistical methods.
• Project Management: Excellent project management and time management skills, with the ability to manage complex, detailed work and effectively communicate progress and results to stakeholders at all levels.

Desirable

• Research: Research experience with peer-reviewed publications.
• Competitions: Recognized achievements in data science competitions, such as Kaggle.
• Certifications: Certifications in cloud-based machine learning services (AWS, Azure, GCP).