Applied Research Data Scientist Python, ML/AI

Analytical Research & Modelling

Employ dimensionality reduction (PCA, UMAP, t- SNE) and unsupervised learning (clustering, mixture models) to discover latent learning traits.
Conduct exploratory data analysis (EDA) on handwriting and voice datasets to identify behavioral patterns and anomalies.
Build and interpret regression models — linear, logistic, mixed effects, LASSO, Ridge — to isolate key factors influencing performance, engagement, or stress.
Perform feature engineering from raw handwriting and audio data (e.g., hesitation index, cognitive delay markers, pitch variability).
Apply causal inference frameworks — backdoor criterion, DAGs, propensity scoring, mediation analysis — to uncover genuine cause- effect linkages.
Quantify uncertainty, confidence intervals, and perform model diagnostics (VIF, residual analysis, cross- validation).
Use multivariate and non- linear regression to examine interdependent behavioral relationships (e.g., writing acceleration vs. tone modulation).

Machine Learning & AI Integration

Experiment with speech emotion recognition, sequence models, or multimodal fusion networks that integrate handwriting + audio.
Use NLP and embedding models to analyze transcribed speech or open- ended answers for affective or cognitive insights.
Collaborate with engineers to prototype LLM- driven insight layers that summarize behavioral findings or explain patterns.
Develop predictive models to forecast engagement, confidence, or completion speed.

Research & Hypothesis Testing

Design and conduct experiments, quasi- experiments, or A/B tests to validate hypotheses and interventions.
Build reproducible research pipelines (Jupyter, MLflow, W&B) with version- controlled analysis and documentation.
Use statistical hypothesis testing (ANOVA, chi- square, t- tests, permutation testing) to verify observed trends.
Apply causal reasoning to determine which variables most strongly influence learning efficiency.

Data Infrastructure & Visualization

Document datasets, model assumptions, and findings in clear technical and narrative formats.
Optimize ETL workflows for handwriting and voice signals, ensuring high- quality data ingestion.
Create interactive dashboards or visualizations (Streamlit, Plotly, Tableau) to communicate insights intuitively.
Work with engineers to maintain clean, labeled, and reliable multimodal data pipelines.

Collaboration & Communication

Support leadership with metrics that guide pedagogy strategy, AI model improvements, and product direction.
Translate analytical outputs into actionable insights for curriculum and product design.
Present complex analyses in simple, visual, and narrative forms for non- technical stakeholders.
Partner with educators and product managers to interpret results in a learning context.

Yêu cầu công việc

Must- Have

Excellent analytical reasoning, hypothesis formulation, and data storytelling skills.
Proven experience in regression modeling (linear, logistic, hierarchical, regularized) and causal inference.
High level of proficiency in English.
Experience in feature engineering, dimensionality reduction, and unsupervised learning.
Strong grasp of statistical hypothesis testing, experimental design, and model diagnostics.
Bachelor’s or Master’s degree in Data Science, Statistics, Machine Learning, AI, Cognitive Science, or related field.
Proficiency in Python (pandas, NumPy, scikit- learn, statsmodels, PyTorch/TensorFlow, causalml, DoWhy).

Good- to- Have

Familiarity with Bayesian modeling, causal ML, or hierarchical models.
Experience with speech signal processing (librosa, OpenSMILE) or handwriting trajectory analysis (CNN/RNN- based stroke models).
Experience integrating data models into AI- driven insights dashboards or feedback loops.
Background in educational data mining, learning analytics, or human performance modeling.
Exposure to LLMs, prompt engineering, and multimodal representation learning.

Tại sao bạn sẽ yêu thích làm việc tại đây

See your models directly influence real students’ growth and learning outcomes.
Competitive salary, benefits, and growth opportunities as the company scales.
Freedom to explore, test hypotheses, and publish findings that advance learning analytics.
Opportunity to build a global movement and shape the frontier of how AI understandshuman learning.
Factors such as qualification, skills, experience, etc. might affect the compensation. Employees are entitled to 14 days annual leave, 6 days sick leave, 6 days hospitalisation leave, 2 days compassionate leave.
Work with a cross- disciplinary team blending education, neuroscience, and AI.
Collaborative, purpose- driven team culture.
Opportunity to work with an experienced team from world- class companies (e.g. Goldman Sachs, TikTok, Accenture, FPT, etc.)

Cập nhật gần nhất lúc: 2025-11-03 08:25:02