Senior Data scientist
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Location
Montreal
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Sector:
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Job Type:
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Contact:
Francisco Lopez
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Job Reference:
4064
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Published:
un an
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Expiry date:
2019-06-26
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Startdate:
2019-07-03
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Consultant:
#
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
- 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
- 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
