About The Position

• Lead on best architectures, best practices, and implementations for end-to-end (from ideation to production and operation) data-science products.
• Developing NLP modules such as NER, POS, entity matching, categorization, text parsers, and more.
• Developing/tuning OCR models and time series prediction models.
• Evaluate technologies, define data pipeline, and processing architectures to allow us to scale machine learning initiatives across April’s products.

• At least 5 years experience with data science projects from early-stage concept to production rollouts.
• [Preferred] At least 2 years of experience with NLP in production
• Experience and desire to do end-to-end
• Previous experience as a Developer/Data Engineer is a significant plus.
• Broad expertise in multiple industries/domains (vs a single field specialist).
• Broad knowledge and experience of data science modeling techniques (from time-series prediction to OCR to RNNs and anything in between) and their use within the industry
• Hands-on experience with data science tooling (TensorFlow, scikit, pyTorch, spacy) and dev environments (PyCharm, Anaconda, yeah Jupyter lab too… But not for production!)
• An all-around data scientist: strong statistics background, strong analytical mindset, experience in experimentation, data visualization, machine learning algorithms, optimization, and big data.
• Experience with Python.
• Experience with SQL
• Advantage: Experience with Cloud Machine Learning Platforms (Google Vertex AI / AWS SageMaker)
• Strong advantage: Kaggle profile

Responsibilities:

• Be part of a data-science team where you will lead end-to-end ML projects: from data extraction through model validation & A/B testing to model deploying.
• Discovering and translating business challenges into data pipelines and model frameworks.
• As first DS, Report directly to
• Work closely with Management, Data-Engineering, and Development teams
• Interface with R&D and data engineering to integrate data collection, data quality, and core model output into production systems.
• Be part of a startup company: Help setting priorities and making design decisions based on your experience and insights.