Best 5 Tips Data Scientists Can Advance Their Careers

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Companies hire data and machine-learning professionals to build cutting-edge ML models that drive real business value.

Yet data scientists often spend up to 80% of their time cleaning data plagued by missing values, outliers, slow load times, and constantly evolving schemas. It is not uncommon for reality to fall far short of expectations.

Data scientists may start out excited to work on advanced models and generate actionable insights, but that enthusiasm quickly fades amid daily schema changes, tables that stop updating, and other silent surprises that break models and dashboards.

The Data Scientist’s Position in the Pipeline

Although “data science” encompasses many roles—from product analytics to deploying statistical models in production—one truth remains constant: data scientists, ML engineers, and analysts typically sit at the very end of the data pipeline. They consume data from warehouses, S3 buckets, or other central repositories and use it to inform business decisions and power machine-learning models.

They feel the impact of poor data quality most acutely, yet they rarely have the authority to fix upstream issues. As a result, they either add heavy data-processing steps to their workflow or abandon the project altogether.

This situation may feel all too familiar. Instead of resigning yourself to broken data engineering processes, adopt an experimental mindset. As the professionals responsible for the final stage—putting models into production—you have a unique opportunity to create meaningful impact.

What might seem like an unfair burden is actually a powerful chance to demonstrate leadership and improve outcomes for the entire organization.

Here are five practical ways data scientists and ML practitioners can move from a defensive posture to a proactive one, ensuring data-quality issues no longer undermine the teams that depend on their work.

1. Strengthen Data Quality Monitoring to Build Trust

Many executives remain reluctant to base strategic decisions solely on data. A 2026 KPMG report showed that 60% of companies lack confidence in their data and 49% of leaders do not fully support internal data or analytics strategies.

By mastering data-quality techniques, data scientists and ML engineers can deliver more accurate information to the dashboards that guide key decision-makers. The result is greater organizational trust and tangible business impact.

Manually auditing data for quality problems is slow and error-prone. Modern data-quality testing frameworks—such as Great Expectations, dbt tests, and data observability platforms—allow teams to detect issues automatically, maintain velocity, and earn stakeholder confidence.

2. Establish SLAs to Eliminate Blame and Confusion

Data-quality incidents often trigger a frustrating blame game among software engineering, data engineering, and data science teams. Who introduced the problem? Who noticed it first? Who will fix it?

Service Level Agreements (SLAs) bring clarity and accountability to every stage of the data pipeline. By defining data-quality expectations in measurable terms and assigning clear owners for remediation, SLAs help teams move forward with reliable information instead of pointing fingers.

3. Enable Faster, More Reliable Experimentation

Trust erodes quickly when stakeholders spot errors or when undetected data issues silently invalidate experiments. Consider a simple labeling inconsistency: one system records “Dallas Fort Worth” while another uses “DFW.” Users in the same geographic segment may be split across different experiment groups, rendering results meaningless.

High-quality data foundations make experimentation faster and more trustworthy. With reliable inputs, teams can focus on interpreting results rather than debugging data pipelines.

4. Become the Point Person for Data Quality

Confidence in data begins with individual ownership. By stepping forward as the go-to expert for data quality and ownership, you can help define standards and coordinate fixes across teams. This leadership reduces friction between engineering and data science while creating organization-wide relief from recurring data headaches.

5. Reduce Data Waste and Infrastructure Costs

Incomplete or unreliable data still occupies terabytes of storage and generates unnecessary compute costs. By identifying and addressing low-quality datasets—especially in high-traffic ML and analytics pipelines—you can reclaim resources through reprocessing, imputation, or selective recollection.

Track the tables you retire, the queries that no longer run on junk data, and the storage savings achieved. Sharing these metrics demonstrates clear value to the broader team.

All data professionals—whether veterans or newcomers—play a vital role in modern organizations. By taking ownership of data reliability, you directly increase the value of every downstream project.

While tools, algorithms, and analytics techniques continue to advance, the underlying data they rely on often remains inconsistent. Even the most sophisticated models cannot overcome fundamentally flawed inputs. By applying the five approaches above, data scientists can improve data quality across the entire company—creating wins for everyone.

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