Metova

Metova

Metova specializes in developing mobile applications and digital solutions, leveraging an agile process to enhance design, usability, and marketing strategies while providing expert consultancy to drive digital transformation for startups and enterpris...

Internet Software & Services
51-250
Founded 2006

Description

  • Lead the design, development, and deployment of data science solutions for large-scale information analysis.
  • Design, train, validate, and deploy machine learning and deep learning models in production environments with big data.
  • Implement anomaly detection and pattern recognition techniques to identify irregularities, fraud, operational risks, or atypical behavior.
  • Execute A/B testing and statistical experimentation to validate hypotheses, measure impact, and optimize products.
  • Collaborate with product, engineering, business, and tax/accounting teams 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 using predictive models, advanced analytics, and generative AI techniques.

Requirements

  • Bachelor's degree in Systems Engineering, Mathematics, Statistics, Computer Science, or a related field; Master's or Doctorate is desirable.
  • 6–12 years of experience in data science, including at least 3 years leading production projects.
  • Strong experience with supervised learning, A/B testing, anomaly detection, and pattern recognition.
  • Experience deploying ML/DL models with millions of records or transactions into production.
  • Python is required; advanced R and SQL experience is also required.
  • Experience with ML pipelines, MLOps, and cloud deployment on AWS, GCP, or Azure.
  • Knowledge of ML/DL frameworks such as scikit-learn, TensorFlow, and PyTorch.
  • Experience with anomaly detection methods such as Isolation Forest, LOF, autoencoders, Prophet, ARIMA, and robust statistics.
  • Experience with predictive modeling and pattern recognition techniques including clustering, time series, sequences, and recurrent neural networks.
  • Experience with SQL and NoSQL databases, plus vector databases such as Pinecone, pgvector, or Milvus.
  • Strong data visualization skills using Matplotlib, Seaborn, Plotly, Power BI, or Tableau.
  • Experience with model testing and cross-validation.
  • Knowledge of tax, accounting, ERPs, or the financial sector is desirable.
  • Experience with NLP and LLMs for information extraction and document classification is desirable.
  • Experience in transaction fraud detection, credit risk monitoring, or tax irregularities is desirable.
  • Familiarity with big data environments such as Spark, Databricks, or Hadoop is desirable.
  • Knowledge of programming languages such as Java, Scala, or C++ is desirable.
  • Publications, presentations, or participation in data science communities is desirable.

Interested in this position?

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