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Sr. Data Science (Time series & Forecasting models)

3 weeks, 5 days ago
Brazil
Senior
Data Scientist
Data Science and Analytics
Deep Learning LSTM Machine Learning Python PyTorch Random Forest RNN Scikit-learn SFTP Statistics TensorFlow XGBoost
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Marathon Talent

Marathon Talent is a human resources and talent advisory firm that helps companies hire top bilingual talent from Latin America through recruitment, fractional people and talent strategy, market insights, salary studies, and advisory/mentorship services.

Human Resources
1-10
Founded 2023
View All Jobs 11

Description

  • Lead forecasting initiatives by designing and implementing advanced time series models for sales and behavioural trends.
  • Develop scalable machine learning solutions for real-time risk management, fraud detection, and product personalization.
  • Collaborate with product managers, engineers, and business stakeholders to turn data challenges into actionable insights and business outcomes.
  • Experiment with advanced approaches such as deep learning architectures, probabilistic forecasting, and transformer-based models.
  • Ensure model development aligns with industry regulations and ethical standards for financial data usage.
  • Identify opportunities where predictive modeling can create new value streams and competitive advantages.
  • Support automated and scalable data-driven decision-making across the business.
  • Contribute to the data science strategy for growth across multiple markets.

Requirements

  • At least 5 years of practical experience with machine learning and deep learning.
  • Strong understanding of fintech products and risk management.
  • Strong foundation in probability, statistics, and econometrics.
  • Experience with time series forecasting methods such as ARIMA, SARIMA, SARIMAX, VAR, exponential smoothing, or state-space models.
  • Experience with machine and deep learning methods such as Random Forest, XGBoost, RNNs, LSTMs, or Transformers.
  • Knowledge of Bayesian approaches such as BSTS, Prophet, or ensemble forecasting.
  • Deep knowledge of sequential data modeling with RNNs, LSTMs, GRUs, or Transformers.
  • Strong proficiency in Python ML/DL frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Comfort consuming data through APIs, SFTP, or CSV files.
  • Experience with explainable AI, especially for deep learning forecasting time series methods.

Interested in this position?

Apply directly on the company website

Apply Now

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