Stats Cat (Founding Statistician / Data Scientist)

Lightup enables data-driven businesses to achieve their full potential. Modern businesses are dealing with unprecedented volumes of real-time data, operating in environments that are constantly changing internally and externally. To get the most out of data-driven operations, businesses need the ability to continuously measure the performance of their data-driven operations at fine granularity across the entire pipeline. Lightup is enabling businesses to continuously track vitals of their data-driven operations at the finest granularity, catch problems before their customers do, and fix them before they impact business.

The founding team brings together a unique combination of experience in big data, stream processing and rigorous statistical signal processing - crucial for building a data stability platform. We have over three decades of industry experience in multiple game changing startups and industry stalwarts like Google, GlobalFoundries and VMware. Lightup is funded by Andreessen Horowitz (a16z) and Spectrum28.

Lightup is looking for systematic stats cats 
  • Masters or PhD in a quantitative discipline (e.g., Statistics, Mathematics, EE, Physics, Computer Science) or equivalent practical experience.
  • 2 years of work experience in data science / analysis related fields

You are well-versed in the following:
  • Statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods
  • Statistical software like  R, Python Pandas, MATLAB
  • Applied experience with machine learning on large datasets
  • Understand engineering performance requirements and translate that to implementation

You also have some experience in:
  • Ability to use Spark (PySpark, SparkSQL) to run data analysis quickly.
  • Articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
  • Striking a balance between statistical rigor and practicality; mastery of deep theory and love for simple stats
  • Designing model performance validation schemes and dealing with lack of labeled data
  • Instrumenting data-driven pipelines for continuous accuracy validation
  • Setting up data experiments and A/B tests in production setting

You might also have experience in: 
  • Database query language like SQL 
  • Work with streaming data pipelines like Kafka, Flink.
  • Knowledge and experience with Tensorflow, Pytorch etc.
  • Understanding of AI/ML techniques

Non-technical attributes: 
  • Demonstrated leadership and self-direction. 
  • Willingness to both teach others and learn new techniques.
  • Demonstrated skills in selecting the right statistical tools given a data analysis problem.
  • Effective written and verbal communication skills.
  • Ability to navigate under ambiguity.

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