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Senior Data scientist

Senior Data scientist

Requirements:
  • Strong communication skills with a proven ability to understand key business and technical concepts, and then effectively communicate these concepts with technical staff, business stakeholders and senior management
  • Strong organizational skills, the ability to perform under pressure and to manage multiple priorities with competing demands
  • Strong analytical, data processing, storytelling and problem-solving skills
  • Experience working in an academic AI research lab
  • Academic publications on Deep Learning, Machine Learning or Operations Research
Studies and experience required
  • A master's degree in Computer Science, Statistics, Mathematics or related fields (A Ph.D. degree is a plus)
  • 5+ years of industry experience as a Data Scientist
  • Experience with Machine Learning, Deep Learning and Reinforcement Learning
  • Experience with Big Data ingestion, processing and visualization
  • Experience with Cloud Native application development
  • Experience of working in banks or financial institutions (FinTech experience is a plus)
  • 5+ years of experience with Python programming language
  • 2+ years of experience with Scala programming language
  • 3+ years of experience with scalable production grade Data Science
  • 3+ years of experience with Scikit-Learn, Pandas, Matplotlib, Numpy, Scipy, and XGBoost
  • Experience with Keras, Tensorflow or Pytorch for Deep Learning
  • Familiarity with Applied Optimization models such as LP, IP or LIP
  • Knowledge of mathematical programming solvers such as CPLEX
  • Experience with Apache Kafka for Event Streaming
  • Experience with Apache Spark (Databricks knowledge is a plus)
  • Experience with NoSQL databases (MongoDB or Cassandra)
  • Knowledge of Graph databases (JanusGraph, Apache TinkerPop or Gremlin)
  • Experience with Agile processes and Software Engineering best practices
  • Experience with Docker, and Kubernetes
  • Experience with DataOps, and AI DevOps
Habiletés:
  • Statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), Principal Components Analysis (PCA)
  • Hypotheses generation about the underlying mechanics of the business process
  • Hypotheses testing using various quantitative methods
  • Networking with business domain experts and product managers to better understand the business mechanics that generated the data
  • Application of various Machine Learning, Deep Learning, Reinforcement Learning, Applied Optimization and Advanced Analytics techniques to perform Classification, Clustering or Regression tasks