Data Scientist II
Seattle, WA /
Engineering – Applied Science /
Full-time/Remote
/ Hybrid
Magnify is a next-generation orchestration platform for the post-sales industry. Over 90% of revenue for most companies comes after the initial sale, but the customer experience is disconnected and siloed across multiple technology platforms and teams. Magnify uses AI/ML along with a modern microservices architecture to transform that experience to drive adoption, retention, and expansion for our customers. The opportunity is a massive and untapped market by current solutions.
Magnify's initial customers are enterprise Chief Customer Officers and their organizations. These executives are challenged to scale the teams and drive performance across increasing numbers of customer touch points, but struggle with fragmented technologies and manual processes. Magnify meets them at their point of need and solves their critical challenges in this large and growing market of over 4 million CS professionals.
Magnify is a fast-moving startup backed by top-tier investors like Madrona and Decibel, with a veteran and successful leadership team.
The Role
As a Data Scientist II, you will build services that sit at the interaction of data architecture and ML services to solve a broad spectrum of problems including data engineering, modeling, automation and experimentation. We scale data science models across a variety of enterprise domains properly. Our Data Scientists are pragmatic leaders who can convert business requirements into scientific solutions from mining structured/unstructured data. You are the one who likes dealing with ambiguity, solving unprecedented problems and building large scale ML and data architecture.As one of the early hires at a venture-track company, this role provides an opportunity to grow with the company with real impact with enormous upside.
Responsibilities
Convert business requirements into concrete solutions
Build centralized data services for large-scale cloud services
Prepare data using ETL/ELT tools for business solutions and ML services
Build ML models to drive faster customer onboarding experience
Monitor the performance and explain the insights from ML models
Identify and standardize areas for improvement in Magnify data infrastructure
Share knowledge with data engineers and scientists on using data and ML techniques
Basic Qualifications
Bachelor’s or Master’s in a science, engineering or quantitative discipline (CS, EE, Math)
3+ years of professional experience in data engineering (architecture, design pattern, reliability and scaling)
2+ years of professional experience in prototyping and automating ML solutions (feature engineering, prototype, testing, automation and monitoring)
Ability to dive deep and identify areas of improvement in existing products, make recommendation, convert into project plans and implement
Ability to prioritize competing tasks in a fast-paced environment for customer needs.
Preferred Qualifications
Master’s in a science, engineering or quantitative discipline (CS, EE, Math)
Experience in building ML and data solutions on cloud services (AWS, Azure, GCP)
Experience in cloud-based relational/no-SQL databases (Snowflake, Redshift, DynamoDB, Neptune), write efficient queries for big data (SQL, Spark)
Experience in properly applying statistical and ML models (supervised/unsupervised learning, reinforcement learning, A/B testing using Python, R with libraries)
Deploy and automate ML services (SageMaker, AirFlow, KubeFlow)Experience in Causal Inference, NLP and Generative AI as a plus
Experience in collaborating with scientists and engineers from multinational teams
Strong verbal and writing and interpersonal skills
Magnify is a values driven culture, we aspire to be among the tech industry's most inclusive work environments. We are committed to diversity in our workforce and are a proud equal opportunity employer. We do not make hiring or employment decisions on the basis of race, color, religion, creed, gender, national origin, age, sex, gender expression or identity, sexual orientation, or disability status, marital status, or veteran status.
