Senior Data Scientist

Haystack News

• $120K — $145K *
Consumer Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • PhD or M.S. in a quantitative field with 5+ years of experience in data science or machine learning
  • 3+ years in large-scale online ranking/recommender systems
  • Deep understanding of statistical inference and experimental design
  • Proficient in causal inference methods
  • Ability to convert offline analysis into impactful product decisions
  • Fluency in Python analytics stack and machine learning tools
  • Strong SQL experience with databases like Postgres or Snowflake

Responsibilities

  • Build models to enhance content discovery and user engagement
  • Collaborate with ML engineers to implement models and insights into production
  • Analyze data to uncover significant business insights
  • Apply causal inference methods for product change impact assessment
  • Create new ML features using text, multimodal embeddings, and GenAI
  • Design and conduct AB tests to validate hypotheses and measure outcomes

Benefits

  • Collaborative work environment with a focus on innovation
  • Opportunity to make meaningful contributions to user experience
  • Dynamic workplace dedicated to the future of news consumption
  • Work alongside a passionate team of professionals
Full Job Description
Join our team at Haystack News as a Senior Data Scientist and become a pivotal force in redefining user experiences through cutting-edge algorithm enhancements. In this role, youll leverage your advanced statistical analysis, modeling, causal inference, experimental design (A/B testing) and data analytics expertise to drive substantial improvements in user engagement and retention, directly impacting our products success. This is an exceptional opportunity to showcase your robust problem-solving capabilities and to thrive in a collaborative environment, working alongside a team of passionate professionals dedicated to innovation and excellence. Be part of a dynamic workplace where your contributions make a meaningful difference and help shape the future of news consumption.

MINIMUM QUALIFICATIONS
  • PhD or M.S. in Computer Science, Mathematics, Electrical Engineering, Statistics, Economics or Operations Research with 5+ years of professional experience in data science, machine learning or related quantitative field
  • 3+ years of professional experience with large-scale online ranking/recommender systems (for news feeds, shopping, ads, music, etc).
  • Deep expertise in statistical inference and experimental design: hypothesis testing, power/sample size calculations, variance reduction, etc.
  • Proficiency in causal inference methods to measure product impact.
  • Proven ability to translate offline analysis into product decisions and measurable improvements in online metrics.
  • Fluency in the Python analytics stack (pandas, NumPy), statistical modeling (statsmodels or scikit-learn) and machine learning packages such as LightGBM and XGBoost.
  • Strong experience with SQL (e.g. postgres, snowflake, etc).

PREFERRED QUALIFICATIONS:
  • Experience working on consumer-facing products with millions of users.
  • Hands-on experience with orchestration/transformation tools (e.g. dbt and Airflow).
  • Experience with deep learning and being familiar with tools such as PyTorch or TensorFlow.
  • Hands-on development of products/tools incorporating GenAI, LLMs, RAG, and/or Agents.


RESPONSIBILITIES
  • Build statistical and machine learning models to improve content discovery and user engagement.
  • Work closely with ML engineers to translate models and insights into production systems.
  • Have curiosity and apply analytical skills to dive deep into data to find key insights that would impact the business.
  • Apply causal inference methods to understand the impact of potential product changes.
  • Define and build new ML features using text and multimodal embeddings and GenAI.
  • Validate offline learnings with online outcomes through AB testing. Design, execute, and analyze experiments to prove product change attribution.

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