Expert Data Science, PCS

Job Description Summary Apply data science, statistical analysis, machine learning and text data mining tech-niques to derive actionable insights under the guidance of senior data scientists and project stakeholders.Works collaboratively with cross-functional teams to prepare, analyze, visualize and interpret data, develop reproducible analytical workflows, and communicate results clearly to technical and non-technical audiences.

Job Description Major accountabilities: Apply standard data science, statistical and machine learning methods to support research, operational and business questions across relevant scientific and functional areas. Perform data extraction, cleaning, transformation, integration and quality checks from multiple structured and unstructured data sources to enable reliable analysis.

Conduct text data mining as a core responsibility, including processing, structuring and analyzing text-based data sources to identify trends, entities, relationships, classifications and insights relevant to project objectives. Support natural language processing activities, including text preprocessing, feature extraction, classification, clustering, entity recognition, topic modelling and evaluation of text-based analytical outputs.

Build, validate and document predictive models , exploratory analyses, dashboards and visualizations under the supervision of more experienced data scientists. Contribute to the development of reproducible analytical pipelines using appropriate programming languages, libraries and version-control practices.

Present analytical findings in a clear, concise and visually effective manner to project teams and business stakeholders, highlighting assumptions, limitations and recommended next steps. Collaborate with scientists, pathologists, data engineers, software engineers and other stakeholders to understand requirements, define analytical approaches and deliver timely outputs.

Support evaluation and adoption of relevant data science tools, packages, visualization approaches and automation opportunities to improve efficiency and quality. Maintain awareness of current developments in data science, machine learning, natural language processing and analytics methods, and apply learnings to assigned tasks where appropriate.

Ensure that work is performed in accordance with applicable data governance, privacy, documentation, quality and compliance requirements Minimum Requirement: Bachelor’s/ master’s degree in data science, Computer Science, Statistics, Mathematics, Bioinformatics, or a related quantitative discipline.

Approximately 2 -4 years of relevant professional experience in data science, applied analytics, machine learning, bioinformatics, computational biology or a related field. Must have practical experience with text data mining , natural language processing or analysis of unstructured text data.

Proficiency in Python and common data science libraries such as pandas, NumPy, scikit-learn, matplotlib, seaborn or equivalent tools. Experience with statistical analysis, exploratory data analysis, predictive modelling and model evaluation.

Experience preparing, cleaning and integrating data from multiple sources. Familiarity with databases and query languages such as SQL. Ability to create clear data visualizations and communicate analytical results to technical and non-technical audiences.

Basic understanding of machine learning concepts including supervised learning, unsupervised learning, feature engineering, model validation and performance metrics. Familiarity with version control, preferably Git, and reproducible coding practices.

Skills Desired Artifical Intelligence (AI) (Inactive), Biostatistics, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Graph Algorithms, Learning Agility, Logistic Regression Model (Inactive), Machine Learning (ML), Machine Learning Algorithms, NLP (NeuroLinguistic Programming) and GenAI (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis