Employment Type
Full-Time
Work Location
Remote (US-Based) · Optional hybrid in New York, NY
Reports To
Co-Founder / CEO
Compensation
$130,000 – $185,000 USD / year
About LockedIn AI
LockedIn AI is the #1 real-time AI interview and meeting copilot, trusted by over 1 million users worldwide. We are building the most advanced career preparation platform that helps candidates succeed in live job interviews, coding assessments, and professional meetings through real-time AI-powered assistance.
Our mission is to make career success more accessible by combining applied AI, machine learning, and human-centered product design.
Role Overview
We are looking for a rigorous, impact-driven Data Scientist to extract meaningful insights from large-scale user data, build predictive models, and apply advanced statistical and machine learning techniques to improve product experience for over 1M users.
This is an applied science role focused on real product impact—not just dashboards. You will design experiments, build predictive systems, develop recommendation and ranking models, and create the quantitative frameworks that guide product, AI, and business decisions.
You will work across product, growth, AI performance, and business domains to answer critical questions such as:
What drives user success in interviews?
Which features improve outcomes the most?
How can we predict churn before it happens?
How do AI improvements impact real user behavior?
Key Responsibilities
1. Advanced Analytics & Statistical Modeling
Build predictive models for churn, LTV, conversion, and engagement
Apply regression, time series, survival analysis, Bayesian methods, and causal inference
Develop user segmentation and clustering models for personalization
Perform deep statistical analysis to support product decisions
2. Experimentation & Causal Analysis
Design and execute A/B tests with rigorous statistical frameworks
Analyze experiments using effect sizes, confidence intervals, and causal inference methods
Identify heterogeneous treatment effects across user segments
Build observational causal inference frameworks when experiments are not possible
3. Machine Learning & Predictive Systems
Build and deploy ML models including ranking, recommendation, classification, and anomaly detection systems
Own end-to-end ML lifecycle: feature engineering, training, deployment, monitoring
Apply NLP techniques to analyze user conversations, feedback, and interview transcripts
Develop scalable feature pipelines for production ML systems
4. AI Performance & Quality Science
Design frameworks to evaluate AI output quality beyond standard metrics
Measure causal impact of model changes and prompt improvements
Build systems to detect model drift and performance degradation
Collaborate with AI engineers to improve model training and evaluation
5. Growth, Retention & Revenue Science
Build churn prediction and retention models
Develop LTV and revenue attribution models
Analyze pricing, monetization, and subscription upgrade behavior
Measure impact of growth experiments and onboarding changes
6. Data Infrastructure & Collaboration
Build reusable data science infrastructure (feature stores, evaluation pipelines, model registries)
Partner with data engineering and product teams to improve data quality and usability
Communicate insights clearly to technical and non-technical stakeholders
Influence product strategy through data-driven recommendations
Required Qualifications
Experience
3+ years in data science, applied ML, or statistical modeling roles
Experience deploying predictive models in production
Strong background in A/B testing and causal inference
Experience working with product and engineering teams in fast-paced environments
Education
Bachelor’s or Master’s in Statistics, CS, Math, Economics, or related field
PhD is a plus but not required
Technical Skills
Strong Python (pandas, NumPy, scikit-learn, statsmodels, PyTorch/TensorFlow)
Advanced SQL for large-scale data analysis
Experience with ML deployment and monitoring
Strong foundation in statistical modeling and causal inference
Experience with BI tools (Looker, Metabase, etc.)
Soft Skills
Strong product intuition and business understanding
Excellent communication and storytelling with data
Ability to work independently in ambiguous environments
Strong ownership mindset from problem framing to deployment
Preferred Qualifications
Experience with NLP, embeddings, or LLM evaluation
Background in recommendation systems or personalization
Experience with advanced causal methods (DiD, IV, RDD, synthetic control)
Experience in SaaS, edtech, or career tech products
Experience with churn modeling and subscription analytics
Research publications or technical writing in data science
What We Offer
Equity
Meaningful early-stage equity and ownership
Impact
Your work directly influences a product used by 1M+ users
Team
Lean, high-performance team where every contribution matters
Flexibility
Remote-first with optional collaboration in New York City
Growth
Fast-paced startup environment with rapid learning opportunities
Culture
User-focused, experimentation-driven, and execution-oriented
Why Join LockedIn AI?
Category-defining AI interview copilot product
Massive and rapidly growing global market
Real production impact of your models on millions of users
AI-native environment using cutting-edge technologies
Fast execution, fast feedback, and high ownership
How to Apply
Please submit:
Resume or CV
Brief note covering:Why you want to join LockedIn AI
Whether you’ve used the product
What improvements you would suggest
Optional: GitHub, portfolio, or technical writing samples
Equal Opportunity
We are committed to building a diverse and inclusive team. Hiring decisions are based on merit, skills, and business needs.
