Land Data Science and ML Engineering Roles in 2026

Data science roles are among the most keyword-specific in the job market. A machine learning engineer role at Meta requires a completely different resume than an ML engineer role at a healthcare company — despite having similar titles. The tech stack, domain focus, scale of data, and business context all vary enough that a generic "machine learning" resume routinely scores below the ATS threshold for either role.

Additionally, data science roles require portfolio evidence (GitHub, Kaggle, publications) that must be properly surfaced in the application. Many portal systems have specific fields for GitHub links and personal websites that automated tools miss entirely.

For each data science application, our VAs extract the specific technical requirements from the job description (model types, cloud platforms like AWS SageMaker or GCP Vertex AI, data pipeline tools like Airflow or dbt, and statistical methods) and rewrite your experience section to reflect those exact terms.

We also complete all supplemental portfolio and project fields in the application portal, ensuring your GitHub and relevant projects are visible to the hiring manager. This level of application completeness consistently outperforms auto-filled applications from tools like Simplify or LazyApply.