Fast Facts

Join Evolv as a Senior Data Scientist, focusing on improving AI/ML systems with deep understanding of sensor data and driving data quality enhancements.

Responsibilities: Analyze high-dimensional sensor data, conduct representation analysis, and ensure data quality through collaboration with data collection teams, while providing actionable insights to improve ML model performance.

Skills: Proficiency in Python and data science libraries, experience with UMAP, t-SNE, PCA, and hands-on analysis of real-world ML datasets like time-series and sensor data.

Qualifications: Master's or PhD in a relevant field; 2-3+ years of practical data science experience; strong understanding of ML fundamentals and model evaluation.

Location: This position is based in Waltham, Massachusetts with a requirement for at least 60% on-site work.

Compensation: $129000 - $209000 / Annually

The Elevator Pitch

Join the Evolv Machine Learning & Sensors team as a Data Scientist focused on driving deep understanding of sensor data, feature spaces, and data quality that power our AI/ML systems. This hands-on role emphasizes representation analysis, exploratory data insights, and data-centric improvements that directly enhance model accuracy, robustness, and generalization. You will work across classical ML and deep learning pipelines to identify blind spots, diagnose data issues, and guide data curation and collection strategies.

Success in the Role: What performance outcomes will you work toward in the first 6–12 months?

In the first 30 days:

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.

Within the first three months:

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

By the end of the first year:

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.

The Work: What type of work will you be doing? What assignments, requirements, or skills will you be performing on a regular basis?

Data Understanding & Representation Analysis:
Analyze high‑dimensional sensor and feature data using UMAP, t‑SNE, PCA, and related techniques.
Identify clusters, outliers, distribution gaps, and blind spots across classes and environments.
Diagnose dataset shift, domain mismatch, sparsity, and representation collapse.

Model‑Aware Data Analysis:

Conduct data analysis aligned with both classical ML models (XGBoost, SVR, k‑NN, tree‑based models) and deep learning models (CNNs, Transformers).
Analyze embeddings, confusion matrices, and failure cases to map model issues back to data causes.

Data Quality & Curation:

Investigate imbalanced data, noisy sensor signals, and mislabeled or ambiguous samples.
Develop strategies for weakly labeled or unlabeled data using clustering or pseudo‑labeling.
Define data quality metrics, acceptance criteria, and labeling strategies.
Work with internal teams and external vendors to improve label consistency and coverage.

Insight‑Driven Improvements:

Translate exploratory insights into clear recommendations for data collection, relabeling, or filtering.
Drive data‑centric improvements instead of relying solely on algorithmic changes.
Track KPIs such as data quality, data quantity, collection rate, and utilization efficiency.

Collaboration & Communication:

Work closely with internal and external data collection teams to refine data pipelines.
Communicate findings through visualizations, reports, and technical deep‑dives.

Qualifications:

Minimum Qualifications:

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.

Preferred Qualifications:

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).

Example Problems You Will Own:

Use UMAP/PCA to detect under‑represented regions of sensor feature space and guide new data collection.
Identify mislabeled samples through clustering and model‑disagreement techniques.
Analyze class imbalance impacts across ML model families and propose data‑centric mitigations.
Cluster large volumes of unlabeled sensor data to support annotation prioritization.

What is leadership like for this role? What is the structure and culture of the team?

You will join our R&D organization, reporting directly to VP of ML and sensors. In this role, you will interface with cross-disciplinary teams of highly skilled and autonomous engineers with expertise in Electromagnetics, Computer Vision, and AI. Our R&D organization includes more than 100 dedicated developers, engineers, scientists, managers and directors, each bringing deep technical knowledge and a strong culture of collaboration and support.
The team culture is one based on building trust, collaboration, on-going development through kindness, authenticity, courage, drive, and fun!

Where is the role located? This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of

our software-enabled hardware products, this position requires a minimum of 60% or 3 days on-site work.

What is the salary range? The base salary range for this full-time position is $129,000 - $209,000. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

· Please note that the compensation details listed in role posting reflect the base salary only, and do not include commission, equity, or benefits

At Evolv, we’re on a mission to help make public spaces safer through innovative security technology. So, we're looking for future teammates who embody our values, people who:

Do the right thing, always;
Put people first'
Own it;
Win together; and continue to
Be bold, stay curious.

Our Benefits Include:

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

Evolv Technology (“Evolv”) is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We welcome and encourage diversity in the workplace, and all employment decisions are made without regard to race, color, religion, national, social or ethnic origin, sex (including pregnancy), age, disability, HIV Status, sexual orientation, gender identity and/or expression, veteran status, or any other status protected by law in the locations where we operate. Evolv will not tolerate discrimination or harassment based on any of these characteristics.

Evolv is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation as part of the job application process, please connect with us at careers@evolvtechnology.com.

Evolv participates in E-verify for all employees after the completion of Form I-9.

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