Position: Senior NLP Engineer
Company: Svitla Systems
Experience required: 3y
Category: Data Science
English level: upper
Published: 2022-03-01T00:00:00+02:00

**Requirements:**
- 3+ years of experience with Python, Data Science, Machine Learning, and NLP.
- Experience building and deploying deep learning models using TensorFlow and/or PyTorch.
- Expertise in NLP.
- You want to join an early-stage startup, or you are incredibly anxious to be challenged at your first startup.
- You like the tension between craft and shipping. You have a strong ability to quickly and effectively evaluate technical tradeoffs and translate them into short/long term business decisions.
- You’ve built highly scalable data pipelines and have deep expertise in setting technical vision, architecting, building, and maintaining performant systems, from launch to scale.
- You are passionate about building / leading data science teams and rapidly developing data pipelines at various stages of growth.
- You pride yourself in communicating complex concepts, including the ability to distill intricate workflows and systems into transparent processes and decisions with measurable company-wide impact.
- You ask “why” a lot and use critical thinking and data to back up your intuitions. You hate when a customer struggles through your product experience.
- You have managed a budget before and have seen first-hand the challenges in managing vendor spending.

**Responsibilities:**
- Code to clean and prepare the data extracted from invoices, including handling nulls, addressing exceptions, imputing data (when possible), and enriching data using third-party APIs.
- Code to validate the goodness of extractions performed by the ML models and humans in the loop.
- In-house models to perform OCR and text classification.
- In-house NLP models to map the extracted data to the client's canonical taxonomy of vendors and line items (e.g., using word embeddings, etc.) at scale.
- In-house NLP models to perform two-way reconciliation between invoice data and the general ledger.
- Work closely with the data engineers and data scientists to produce the data science and machine learning code developed. You will work closely with the engineering team to ensure the data science and machine learning pipelines are performant in production.
- Data science vision – ownership over and definition of the data science pipeline, working closely with the executive team, and acting as a thought leader across the company.
- Technical execution – define, articulate, and execute the data science strategy from launch to scale, manage day-to-day performance, and implement best practices.
- Fast prototyping culture – Develop data science and machine learning applications fast, prototyping quickly to solve thorny business problems without getting bogged down in theory (ship early and often).
- Getting your hands dirty - there is a small team, and they have a lot of work ahead of us. You should be excited to roll up your sleeves and help the team in any way you can.
- Build, build, build – jumping in and writing data science code, from architecture to fixing bugs, delivering NLP models and insights within the first three months of hire. The client relies on you and your team to handle day-to-day execution, including:


- Clean the extracted data, validating the accuracy of the extraction, and addressing issues as they occur;
- Ship NLP applications to perform mapping of extracted data at scale;
- Develop and deploy models using PyTorch or TensorFlow;
- Perform feature engineering on the cleansed and mapped data;
- Generate insights and narratives;
- Wrangle, standardize, enhance, implement, and monitor data repositories;
- Create workflows to ingest, enrich, and make data available across the client’s platform.