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Senior Data Science Engineer
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Collaborate with cross-functional teams to design, develop, and implement AI features to enhance user safety and trust. Leverage machine learning techniques to build predictive models for identifying potential abusive patterns in user behavior.
Anomaly Detection
Lead the design and implementation of anomaly detection systems to identify deviations from normal user behavior. Develop algorithms and models to recognize and mitigate potential abuse scenarios.
Data Analysis
Conduct in-depth analysis of large datasets to extract meaningful insights related to user behavior and safety trends. Work closely with Data Engineers to ensure data quality, integrity, and accessibility for analysis.
Collaboration
Collaborate with cross-functional teams, including product managers, software engineers, and security experts, to integrate safety and trust features seamlessly into our products.
Model Evaluation and Optimization
Evaluate the performance of machine learning models regularly and implement improvements as needed. Optimize algorithms for scalability, efficiency, and accuracy in real-time anomaly detection scenarios.
What does it take to work at LastPass?
Background in Computer Science, Data Science, Statistics, or a related field. Proven experience in developing and deploying machine learning models, with a focus on anomaly detection. Proficiency in programming languages such as R, Python, and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch). Familiarity with data/ML technologies (e.g., Spark, Hadoop, DataBricks, MLflow) and distributed computing. Strong understanding of statistical modeling, data mining, and machine learning algorithms. Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders. Good problem-solving skills, team player, and can-do attitude. Ability to communicate comfortably with different stakeholder groups with different backgrounds and technical understanding within LastPass. Good written and verbal communication skills in English.
It's great, but not required:
Experience working on projects related to online safety, trust, or abuse prevention. Familiarity with tooling related to abuse detection (e.g., Azure Sentinel, DataDog, Crowdstrike or Splunk) Knowledge of privacy and ethical considerations in data science. Experience working with global teams.
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