The Senior Data Scientist will lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
Level: L5
Experience: 8–15 Years
Location: Pune, India
Work Type: Hybrid (2 days onsite)
Reports To: Engineering Manager, Data Platform
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Equal Opportunity Employer
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
