Develop a strong understanding of Evolv’s sensor ecosystem, datasets, and ML pipelines.
Review dataset structure, labeling processes, and existing exploratory data analyses.
Run initial UMAP/PCA/t-SNE analyses to map data distributions and identify anomalies.
Identify opportunities to improve data quality, labeling consistency, and dataset coverage.
Perform deep representation analysis across sensor, time‑series, and feature data.
Evaluate classical ML and deep learning models by linking model errors to data issues.
Define data quality metrics and initial dataset acceptance criteria.
Collaborate with data collection teams to guide targeted data acquisition and relabeling.
Data mining on existing field data and understanding patterns and extract useful information and insights.
Design methods to improve data quality, converting noisy/unverified data into clean/verified data.
Own data‑centric insights that directly improve ML model performance.
Establish ongoing monitoring of data drift, blind spots, and label quality.
Provide strategic guidance for future data collection, annotation, and curation.
Develop automated tools and dashboards for data quality reporting and representation analysis.

Requirements

Master’s or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, Physics, or related field
2-3+ years of data science experience working with real‑world ML datasets (time‑series, images, video, sensors)
Proficiency in Python and data science libraries (NumPy, pandas, matplotlib, seaborn)
Hands‑on experience using UMAP, t‑SNE, PCA, or other representation analysis methods
Experience analyzing data for both classical ML and deep learning models
Strong understanding of ML fundamentals and model evaluation methodologies.
Experience with sensor or time‑series data (magnetic, radar, 3D, environmental, IoT)
Familiarity with scikit‑learn workflows and preprocessing techniques.
Experience addressing imbalanced datasets, label noise, and data drift.
Knowledge of embedding analysis, feature importance, and model interpretability.
Experience collaborating with annotation or data collection teams.
Familiarity with MLOps or data versioning tools (MLflow, W&B, DVC).

Benefits

Equity as part of your total compensation package
Medical, dental, and vision insurance
Health Savings Account (HSA)
A 401(k) plan (and 2% company match)
Flexible Paid Time Off (PTO)- take the time you need to recharge, with manager approval and business needs in mind
Quarterly stipend for perks and benefits that matter most to you
Tuition reimbursement to support your ongoing learning and development
Subscription to Calm

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data sciencemachine learningdeep learningdata miningdata quality metricsrepresentation analysisdata preprocessingembedding analysisfeature importancemodel evaluation

Soft Skills

collaborationstrategic guidanceproblem-solvinganalytical thinkingcommunication

Certifications

Master’s in Data SciencePhD in Data ScienceMaster’s in Computer SciencePhD in Computer ScienceMaster’s in Applied MathematicsPhD in Applied MathematicsMaster’s in StatisticsPhD in StatisticsMaster’s in PhysicsPhD in Physics