Data Scientist

LockedIn AI is a fast-growing AI-native SaaS (Software-as-a-Service) company operating in the career tech and edtech space. The company builds real-time AI interview and meeting copilots used by over 1 million users globally. Employment Type Full-Time Work Model Remote (US-Based) with optional hybrid work in New York, NY Compensation $130,000 – $185,000 USD per year Level of Experience Mid-Level to Senior (3+ years of experience in data science, applied machine learning, or statistical modeling required) About the Company LockedIn AI is a leading AI-powered career platform designed to assist users during live job interviews, coding assessments, and professional meetings. The platform delivers real-time AI support that helps users improve communication, confidence, and performance in high-stakes scenarios. The company serves over 1 million users worldwide and focuses on building advanced AI-driven systems that combine real-time intelligence with scalable product infrastructure. Role Overview The Data Scientist will play a critical role in extracting insights from large-scale user data, building predictive models, and applying statistical and machine learning techniques to improve product performance and user outcomes. This is an applied science role that goes beyond reporting dashboards. You will design experiments, develop predictive systems, build recommendation engines, and create frameworks that guide product, AI, and business decisions. You will work across product, growth, AI performance, and revenue domains to answer key questions such as user success drivers, churn prediction, and AI feature impact. Key Responsibilities Advanced Analytics & Statistical Modeling Build predictive models for churn, conversion, and user lifetime value Apply regression, time series, Bayesian methods, and causal inference Develop user segmentation and behavioral clustering models Analyze key product and business performance drivers Experimentation & Causal Analysis Design and run A/B tests and controlled experiments Perform statistical analysis of experiment outcomes Measure causal impact of product and AI changes Develop frameworks for observational causal inference Machine Learning & Predictive Systems Build and deploy ML models for recommendations and ranking Develop classification, anomaly detection, and forecasting systems Perform NLP analysis on user feedback and interaction data Maintain ML feature engineering pipelines AI Performance & Quality Science Build frameworks to evaluate AI system quality and performance Measure impact of model updates and prompt changes Identify AI drift and performance degradation early Collaborate with AI engineers to improve model outputs Growth & Revenue Analytics Build churn prediction and retention models Optimize pricing and revenue strategies using data Analyze acquisition funnels and marketing effectiveness Measure impact of growth experiments Data Infrastructure & Collaboration Build reusable data science frameworks and tools Work closely with engineers and product teams Translate complex data insights into business decisions Support leadership with actionable analytics Qualifications Required Experience 3+ years in data science, applied machine learning, or analytics roles Experience building production-level predictive models Strong background in A/B testing and experimental design Experience working in cross-functional product environments Familiarity with startup or high-growth environments Education Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Economics, or related field PhD is optional and not required Technical Skills Python (pandas, NumPy, scikit-learn, statsmodels) SQL (advanced level) Machine learning frameworks (PyTorch or TensorFlow) Statistical modeling and causal inference techniques Experience with BI tools (Looker, Metabase, etc.) ML deployment and monitoring experience Soft Skills Strong analytical and problem-solving mindset Ability to communicate complex findings clearly Product-oriented thinking Self-driven and comfortable in fast-paced environments Strong ownership from problem definition to execution Preferred Qualifications Experience with NLP and text analytics Knowledge of recommendation systems and personalization Advanced causal inference methods beyond A/B testing Experience in churn modeling and retention analytics Background in SaaS, edtech, or career tech products Published research or technical writing in data science