Senior Data Scientist - NLP
We’re on a mission to make app building so easy everyone can do it – regardless of their background, tech knowledge or budget. We’ve already helped thousands of entrepreneurs, small businesses and even global brands, like the BBC, Makro and Pepsi achieve their software goals and we’ve only just started.
Builder.ai was voted as one of 2023’s ‘Most Innovative Companies in AI’ by Fast Company, and won Europas 2022 ‘Scaleup of the Year’. Our team has grown to over 800 people across the world and our recent announcement of $250m Series D funding (and partnership with Microsoft) means there’s never been a more exciting time to become a Builder.
Life at Builder.ai
At Builder.ai we encourage you to experiment! Each role at Builder has unlimited opportunities to learn, progress and challenge the status quo. We want you to help us become even better at supporting our customers and take AI app building to new heights.
Our global team is diverse, collaborative and exceptionally talented. We hire people for their differences but all unite with our shared belief in Builder’s HEARTT values: (Heart, Entrepreneurship, Accountability, Respect, Trust and Transparency) and a let’s-get-stuff-done attitude.
In return for your skills and commitment, we offer a range of great perks, from hybrid working and a variable annual bonus, to employee stock options, generous paid leave, and trips abroad #WhatWillYouBuild
Why you should join
As we continue to innovate and evolve, our Intelligent Systems team, is seeking a dynamic Senior Data Scientist with a specialized focus on Natural Language Processing (NLP). In this role, you will collaborate closely with global product and engineering teams across the work which spearheads groundbreaking initiatives in data science, machine learning, and AI to drive intelligent decision-making, and we anticipate significant growth in the coming year and beyond.
This role offers ownership of a myriad of existing use cases and an exciting opportunity to explore ideas currently residing within the realm of pure research. Key challenges include:
- Automatic Speech-to-Text Transcription: Develop state-of-the-art models for accurately transcribing audio calls between customers, partners, and colleagues.
- Feature Extraction from Transcripts and Documents: Create innovative techniques to extract informative entities and features from call transcriptions and documents.
- App Template Recommendations: Utilize NLP to recommend app templates based on customer ideas and requirements.
- Feature Recommendations: Develop models to recommend app features based on customer-provided descriptions and requirements.
- Conversational AI Engagement: Implement conversational AI solutions, such as chatbots, to engage customers and colleagues, gather requirements, create project "buildcards," and provide project progress updates.
- Custom Speech Recognition Models: Build and refine custom speech recognition models to enhance transcription accuracy.
- Custom Language Models: Construct bespoke language models to deepen our understanding of semantics within the Builder domain.
- Customer Question Answering: Create models to automatically answer customer questions, enhancing engagement and support.
We are expanding our IS-NLP team, responsible for managing NLP services like Template Recommendation, Feature Search, Story Similarity, Feature Tagging, and Natasha. As we broaden our service portfolio to streamline Builder delivery processes, we are eager to expand our team to align with our ambitious goals.
- Entrepreneurial Mindset: Demonstrate an entrepreneurial spirit and a "can-do" attitude, thriving in a dynamic and innovative environment.
- Python Proficiency: Proven expertise in programming with Python, showcasing your ability to build robust data science solutions.
- Data Manipulation Skills: Real-world experience in data querying using SQL, data manipulation, and feature engineering to extract valuable insights from complex datasets.
- Data Science Libraries: Proficiency with essential data science libraries such as Pandas, Numpy, Scipy, and Seaborn for data analysis and visualization.
- Deep Learning Expertise: Hands-on experience with Deep Learning libraries, particularly PyTorch and HuggingFace, demonstrating competence in advanced machine learning techniques.
- NLP Proficiency: Familiarity with NLP toolkits like Spacy, NLTK, and TextBlob, and a track record of solving NLP problems, including text classification, named entity recognition, search, and recommendation.
- Version Control and CI/CD: Proficiency in using GitHub and CI/CD pipelines for automated deployment of data science solutions.
- MLOps: Experience in MLOps, encompassing setting up model monitoring and optimization, ensuring models perform at their best.
- Web Services: Knowledge of web services frameworks like FastAPI and Flask for hosting models and integrating them with existing services via RESTful APIs.
- Communication Skills: Excellent communication skills with the ability to engage and present effectively to diverse stakeholders.
- Interdisciplinary Collaboration: Proven capability to work collaboratively within interdisciplinary teams comprising product, engineering, business, and technology experts.
- Advanced Education: A PhD or advanced Master's degree in a scientific discipline such as Statistics, Computer Science, Operational Research, Mathematics, or Physics.
- Machine Learning Expertise: Experience in one or more advanced machine learning areas, including Supervised Learning, Deep Learning, Probabilistic Inference, Statistical Modeling, Bayesian Statistics, Unsupervised Learning, and Reinforcement Learning.
- Passion for Software Development: A strong passion for software development and engineering, complementing your data science skills.
- Industry Experience: 2-4 years of industry experience, with a proven track record of taking concepts and models from conception to production, quantifying their business impact.
- Consumer/Product Expertise: Previous experience in a consumer, product, or eCommerce business is beneficial, showcasing your ability to address real-world challenges.
- Academic Research: Academic research experience is advantageous, demonstrating your capacity to propose bespoke and innovative solutions to non-standard machine learning problems.
- Containerization and Orchestration: Proficiency in working with Docker and Kubernetes technologies, enabling efficient deployment and management of data science solutions.
- Cloud Expertise: Knowledge of and experience with cloud technologies, including Azure and AWS, facilitating scalability and accessibility of data science solutions.
- Industry Recognition: A track record of industry recognition, which may include high-impact academic research contributions, significant contributions to open-source projects, or exceptional performance in competitions like Kaggle.
- Attractive performance related quarterly bonus
- Stock options in a $250 million funded Series D scale-up company
- 24 days annual leave + bank holidays
- 2 x Builder family days each year
- Time off between Christmas and New Year
- Generous pension contributions
- Private medical & dental insurance provided by AXA
- Access to our Perkbox
- A "work from home" equipment allowance