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Lead Data Scientist (14431851)

aKube, Inc. • Remote • Posted 3 days ago

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Remote • Full-time • $103.50/hr • Senior Level

Job Highlights

Using AI ⚡ to summarize the original job post

The Lead Data Scientist at aKube, Inc. will drive ground-breaking innovation and apply state-of-the-art machine learning across various aspects of advertising, including inventory forecasting, planning, pricing, targeting, decisioning, and efficient ad delivery. This role involves collaborating with cross-functional teams to identify business opportunities, translating business questions into data science frameworks, and developing scalable and efficient methods for large-scale data analysis and model development. The position requires a strong statistical and mathematical background, proficiency in Python, Java, Scala, and large-scale ML/DL platforms, and a passion for technology and innovation.

Responsibilities

  • Drive ground-breaking innovation and apply state of the art machine learning in a variety of areas to boost in every aspect of advertising.
  • Collaborate with cross-functional teams to identify business opportunities, and translate business questions into data science framework.
  • Leverage advanced data analytics skills to extract key insights.
  • Develop scalable and efficient methods for large scale data analysis and model development.
  • Build and experiment brand new algorithms and models e2e throughout production rollout and continuous optimization.
  • Mentor team members and guide their technical development.

Qualifications

Required

  • 6+ years of hands-on experience in machine learning and advanced analytics.
  • Experience in the advertising domain is preferred.
  • Solid understanding of ML technologies, mathematics and statistics.
  • Proficient with Python, Java, Scala, large scale ML/DL platforms and processing tech stack.
  • Passion to understand the ad business and apply accurate research study according to the business scenario.
  • Passion for technology, open to interdisciplinary work, and experience in building data-driven services and applications.
  • Proven track record of thriving in a fast-paced, data-driven, collaborative and iterative applied research environment is required.

Preferred

  • Experience in digital video advertising or digital marketing domain
  • Experience with CTR/CVR model, generative AI
  • Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment
  • Experience engineering big-data solutions using technologies like EMR, S3, Spark, Databricks
  • Experience loading and querying cloud-hosted databases such as Snowflake
  • Familiarity with automated deployment, AWS infrastructure, Docker or similar containers

Full Job Description

Job Description
City: Seattle, WA

Onsite/ Hybrid/ Remote:Remote, woud need to go on-site once in a while.

Duration:18 months

Rate Range: $103.5/hr on W2 depending on experience (no C2C or 1099 or sub-contract)

Work Authorization: GC, USC, All valid EADs except H1b

Top Notes:

The team handles research for Ad Platforms by extracting data from data sets to understand opportunities/ad hoc requests

Nice to have is ad platforms experience.

  • Python preferred
  • Statistical background
  • AWS
  • Tableau
  • Azure
  • Visualization tools -building out dashboards - will help to redesign
  • Data Science analysis
  • Data analytics
  • statistical analysis
  • 5-8+years experience
  • data pipelines

Description:

Ad Platforms organization is fully responsible for building, enhancing and maintaining the high-performance, distributed, microservice-based Advertising Platform across all of online properties. We build and maintain proprietary technology, ranging from ad serving and ad delivery, campaign management, reporting as well as all the integrations internal and external that come with evolving and maintaining a best-in-class video advertising business.

The Ad Intelligence team is under Ad Platforms and its mission is to transform advertising and Ad platform with data and AI across TV and streaming video. We build solutions to measure and optimize every aspect of the advertising life cycle. Our tenant is a strong cross-domain team to deliver E2E solutions covering tech areas ranging from machine learning, big data, microservices to data visualization. Our team is seeking a lead data scientist who will be an outstanding addition and leading development for prediction or optimization engines for addressable ad platforms

WHAT YOU'LL DO:
• Drive ground-breaking innovation and apply state of the art machine learning in a variety of areas to boost in every aspect of advertising, including inventory forecasting, planning, pricing, targeting, decisioning, and efficient ad delivery.
• Collaborate with cross-functional teams to identify business opportunities, and translate business questions into data science framework. Leverage advanced data analytics skills to extract key insights.
• Develop scalable and efficient methods for large scale data analysis and model development.
• Build and experiment brand new algorithms and models e2e throughout production rollout and continuous optimization.
• Mentor team members and guide their technical development.

Basic Qualifications:
• 6+ years of hands-on experience in machine learning and advanced analytics. Experience in the advertising domain is preferred.
• Solid understanding of ML technologies, mathematics and statistics.
• Proficient with Python, Java, Scala, large scale ML/DL platforms and processing tech stack.
• Passion to understand the ad business and apply accurate research study according to the business scenario, and seek innovation opportunities to enhance business effectiveness.
• Passion for technology, open to interdisciplinary work, and experience in building data-driven services and applications.
• Proven track record of thriving in a fast-paced, data-driven, collaborative and iterative applied research environment is required.

NICE-TO-HAVES:
• Experience in digital video advertising or digital marketing domain
• Experience with CTR/CVR model, generative AI
• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment
• Experience engineering big-data solutions using technologies like EMR, S3, Spark, Databricks
• Experience loading and querying cloud-hosted databases such as Snowflake
• Familiarity with automated deployment, AWS infrastructure, Docker or similar containers