Data Scientist
About The Job
In a world of assessments which can be outsmarted by guesswork and prefab answers, SpeakNow is the only solution in the language testing space to measure real life communication capabilities by evaluating natural speech and writing.
Using a combination of cutting-edge technology and advanced linguistics research, the Speaknow test is designed to measure English quickly and accurately. Our test is used by leading organizations around the world to assess English abilities in recruitment and language teaching, and is trusted by educational institutions and government offices to qualitatively measure English abilities in a wide variety of cases.
What will you do with us?
SpeakNow is looking for a learning oriented Data Scientist to join our R&D team.
Your goal is to ensure our models are state of the art in their accuracy and reliability for English language assessment.
You will be the bridge between raw data and model performance. Your work will directly impact how we evaluate language proficiency for candidates worldwide.
Key Responsibilities
- Train state-of-the-art models to assess language proficiency.
- Test Performance Analysis: Detect areas where current models underperform in the test. Use error analysis and statistical methods to identify biases or accuracy gaps.
- Dual-Domain Optimization: Work across both Audio and NLP to ensure a holistic evaluation of the user’s language capabilities.
- Collaboration: Work closely with our linguistic experts to translate qualitative language rubrics into quantitative model improvements.
- Deploy production grade solutions to support tens of thousands of users worldwide.
Requirements
- Educational Background: BS or MS in Data Science, Computer Science, Statistics, or a related field.
- Experience: 3+ years of industry experience as a Data Scientist, specifically working with Machine Learning.
- Technical Proficiency: Strong programming skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
- Analytical Mindset: Experience in model evaluation and error analysis.
- Communication: Proficient English and the ability to explain complex technical findings to non-technical stakeholders.
Advantages (Nice to Have)
- Linguistic Knowledge: Experience in Computational Linguistics and NLP.
- Audio Experience: Familiarity with audio signal processing.
