Senior Applied Data Scientist — Data Classification

Senior Applied Data Scientist — Data Classification

Location: United States — SF Bay Area preferred

Engagement: Full-time commitment; initial three-month contract-to-hire

Compensation: $110,000–$130,000 annualized, based on experience, paid monthly

About Symosis Security

Symosis Security is a Silicon Valley–based cybersecurity consulting firm helping enterprise and public-sector clients strengthen security through AI security, security engineering and automation, advisory, and managed security services. Our senior practitioners work directly with clients to solve complex problems and deliver measurable results.

We’re expanding our AI and data security practice and looking for an experienced, hands-on data scientist who wants technical ownership, direct client exposure, and opportunities to help grow the practice.

About the Role

You will lead ground-truth development and performance evaluation for an enterprise sensitive-data classification engagement. Working alongside a technical lead and ML engineer, you will identify classification gaps, guide improvements, and independently validate results.

This role requires practical experience evaluating classification systems and translating analysis into engineering actions.

What You’ll Do

• Develop representative ground-truth datasets, sampling plans, and labeling guidelines.
• Define positive, negative, and edge-case examples for assigned data categories.
• Review ambiguous labels and resolve reviewer disagreements.
• Measure baseline and final precision and recall using the client’s methodology.
• Analyze false positives, false negatives, sampling bias, and coverage gaps.
• Partner with engineers to prioritize classifier improvements.
• Design holdout evaluations and check for overfitting and data leakage.
• Document methodology, results, limitations, and recommendations.
• Present findings clearly to client technical stakeholders.

What You Bring

• 6+ years of relevant applied data science or machine learning experience.
• Strong Python, SQL, and statistical analysis skills.
• Hands-on experience evaluating classification systems using precision, recall, and error analysis.
• Practical experience with sampling, ground-truth development, labeling quality, and validation.
• Ability to translate analytical findings into actionable classifier improvements.
• Experience working with enterprise datasets and existing tools or frameworks.
• Clear communication and comfort working directly with client stakeholders.

Preferred Experience

• Sensitive-data discovery, DLP, privacy engineering, security analytics, fraud detection, or content classification.
• Evaluation of rule-based, ML-based, or hybrid classification systems.
• Building repeatable evaluation pipelines and quality checks.
• Prior experience at Google, Microsoft, Meta, Amazon, Apple, NVIDIA, Tesla, or a comparable technology organization.
• SF Bay Area location.

We prioritize demonstrated technical ownership and delivery over employer names.

Compensation & Employment Structure

Compensation is $110,000–$130,000 annualized, based on relevant experience and qualifications, paid monthly during the initial three-month contract period. Partial months will be prorated.

The initial contract requires a full-time commitment and includes no company-provided employee benefits. Following a review of performance and ongoing business needs, the role may convert to permanent full-time employment with Symosis’s standard employee benefits. Compensation will be reviewed at conversion.

Work must be performed within the United States, with availability for Pacific Time collaboration. SF Bay Area candidates are preferred; remote arrangements are subject to client approval.

Please include:

• Location: City/state and availability to collaborate during Pacific Time business hours.
• Start date: Earliest available start date and any notice period.
• Compensation: Confirmation that the posted compensation range and initial three-month contract-to-hire arrangement work for you.
• Relevant experience: A brief example of a classification project you evaluated or improved, including your role, methodology, and measurable results. Please do not share confidential client information.