Band

Level 3

Job Description Summary

#LI-Hybrid
Location: Cambridge, MA

Job Description Summary

The Oncology Data Science (OncDS) team in Biomedical Research provides computational biology, AI, and data expertise and brings together projects spanning the entire pre-clinical to clinical development pipeline across a wide and unique range of novel therapeutics. OncDS specializes in using high-throughput genomic and biomarker data for target identification, drug discovery, and clinical development.

We are seeking a motivated and curious Scientific Data Engineer at the beginning of their professional data journey to support the development, maintenance, and modernization of FAIR oncology reference datasets. This role combines hands-on data curation with workflow automation, AI-enabled process improvement, dataset refresh and quality control, and fit-for-purpose data engineering to help ensure that key oncology data assets are reliable, reproducible, and ready for downstream scientific use.

This position will work closely with senior team members and cross-functional collaborators to modernize data curation processes, identify opportunities for AI-enabled automation, and contribute to high-value reference datasets that support Oncology-wide data strategy and FAIR data goals. The role is designed to build practical experience in scalable data practices, operational excellence, and automation-driven continuous improvement.

Why join us: This is an opportunity to work at the intersection of data engineering, computational biology, and oncology research, contributing to data assets that enable important scientific decisions across the drug discovery pipeline. You will join a collaborative team that values curiosity, learning, and continuous improvement, and you will have the chance to grow your technical and scientific skills while helping build reliable, scalable data foundations for cutting-edge oncology research.

 

Job Description

Key responsibilities:

• Work with senior team members to identify opportunities for simplification, automation, and continuous improvement in operational data processes, including modernization of data curation workflows

• Support the development and improvement of automated workflows for data processing, quality control, recurring dataset updates, and AI-enabled process improvement

• Collaborate with cross-functional teams to curate, maintain, and improve key oncology reference datasets, with an emphasis on quality, correctness, reproducibility, and operational reliability

• Contribute to reference dataset lifecycle management, including documentation, versioning, and traceable update processes

• Assess dataset structure, integrity, and fitness for downstream scientific use, including attention to identifiers, primary keys, missing values, consistency, and related data quality issues, and help resolve data issues in collaboration with relevant partners

• Contribute to best practices for reproducible workflows, data handling, and scalable dataset operations

• Help keep oncology reference datasets current, usable, and responsive to evolving scientific and organizational priorities

Essential Requirements:

• Degree in bioinformatics, computational biology, data science, computer science, or a related field, or equivalent relevant experience

• Experience working with omics datasets in a research environment

• Proficiency in one or more programming languages used in data science or bioinformatics, such as Python or R

• Experience with data wrangling, data quality control, and reproducible analysis workflows

• Familiarity with workflow automation and scripting for recurring data processing tasks

• Familiarity with database principles and sound data modeling practices, including normalization, primary keys, joins, and thoughtful handling of missing values and data integrity issues

• Knowledge of version control and Unix / Linux-based working environments

• Strong organizational skills and attention to detail, with a demonstrated interest in operational excellence

• Ability to learn quickly, manage multiple tasks, and work effectively in a collaborative environment

• Excellent written and verbal communication skills, and the ability to work effectively across technical and scientific teams

Desirable Requirements:

• Experience with oncology datasets, translational research data, or biomedical reference data resources

• Familiarity with FAIR data principles, metadata curation, or scientific data stewardship

• Experience maintaining reference datasets or supporting data products used by multiple stakeholders

• Experience collaborating with experimental scientists or lab-based partners

Novartis Compensation and Benefit Summary: 

The salary for this position is expected to range between $103,600 and $192,400 per year.

The final salary offered is determined based on factors like, but not limited to, relevant skills and

experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published

salary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of the

role, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life and

disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.

 

Salary Range

$103,600.00 - $192,400.00

 

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