We are looking for a Senior Data Scientist to lead the design, development, and deployment of data science solutions geared toward large-scale information analysis. The role requires proven experience bringing machine learning and deep learning models to production with massive data, applying A/B testing, supervised learning, anomaly detection, and pattern recognition practices.

The ideal candidate should be hands-on, with a solid background in statistics, algorithms, and programming, and capable of translating business problems (especially in the accounting, financial, and tax domains) into scalable, secure, and high-impact solutions.

Responsibilities

Design, train, validate, and deploy machine learning and deep learning models in production environments with big data
Implement advanced anomaly detection and pattern recognition techniques to identify irregularities, fraud, operational risks, or atypical behavior in the data
Execute A/B testing and statistical experimentation to validate hypotheses, measure impact, and optimize information analysis products
Collaborate with cross-functional teams (product, engineering, business, tax/accounting) to translate needs into data science use cases
Ensure data quality through pipeline cleaning, validation, orchestration, and monitoring processes
Develop and maintain technical documentation, metrics dashboards, and model performance reports
Propose new solutions based on predictive models, advanced analytics, and generative AI techniques that add strategic value

Profile Requirements

Academic

Bachelor's degree in Systems Engineering, Mathematics, Statistics, Computer Science, or related field (Master's/Doctorate desirable)

Experience

6-12 years of experience in data science, with at least 3 years leading projects in production
Solid experience in supervised learning, A/B testing, anomaly detection, and pattern recognition
Experience putting ML/DL models with millions of records or transactions into production

Technical

Languages: Python (required), R, and SQL (advanced)
Experience with ML pipelines, MLOps, and cloud deployment (AWS, GCP, or Azure)
Knowledge of ML/DL frameworks (scikit-learn, TensorFlow, PyTorch)
Experience with anomaly detection (Isolation Forest, LOF, autoencoders, Prophet, ARIMA, robust statistics)
Experience in pattern recognition and predictive modeling (clustering, time series, sequences, recurrent neural networks)
SQL and NoSQL databases; experience with vector databases (Pinecone, pgvector, Milvus).
Strong data visualization skills (Matplotlib, Seaborn, Plotly, Power BI, Tableau).
Experience with model testing and cross-validation

Plus / Desirable (Nice to Have)

Knowledge of tax, accounting, ERPs, or the financial sector (banks, fintechs, insurance companies)
Experience in NLP and LLMs for information extraction and document classification
Experience in transaction fraud detection, credit risk monitoring, or tax irregularities
Familiarity with big data environments (Spark, Databricks, Hadoop)
Knowledge of programming languages such as Java, Scala, C++
Publications, presentations, or participation in data science communities