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Senior/Lead Data Scientist

Banque Misr · Le Caire

New
Senior 🇬🇧 English
Python Java Scala SQL Hadoop Spark Hive Impala Redis logistic regression random forest boosting neural networks statistical analysis variable selection dimensionality reduction

Job description

About the role

We are looking for a Senior/Lead Data Scientist to join an agile team that shapes customer experiences through machine‑learning and predictive modelling. The role works closely with product design, engineering and business stakeholders to turn data challenges into actionable insights.

Key responsibilities

  • Collaborate with product, design and engineering to understand business needs and translate them into data‑driven solutions.
  • Research, design and implement innovative statistical and machine‑learning models for predictive analytics.
  • Communicate findings and recommendations to business stakeholders in clear, actionable terms.
  • Prioritise deliverables for each use‑case together with the Data Scientist lead.
  • Design, deploy and evaluate predictive models and advanced algorithms that drive business decisions.
  • Contribute to data‑architecture decisions and work with technology teams to operationalise models.
  • Perform data processing tasks such as statistical analysis, variable selection and dimensionality reduction on large data sets.

Required profile

  • Bachelor’s degree in Economics, Computer Science, Engineering, Statistics, Mathematics, Physics, Operations Research or a related discipline with strong academic record.
  • Master’s degree in Computer Science or Statistics is a plus.
  • Proven experience working with large‑scale data platforms (Hadoop, Spark) and distributed databases (Hive, Impala, Redis).
  • Ability to work cross‑functionally and communicate technical concepts to non‑technical audiences.

Required skills

  • Programming languages: Python (preferred), Java, Scala, R.
  • SQL proficiency.
  • Machine‑learning algorithms: linear regression, logistic regression, random forest, boosting, neural networks.
  • Big‑data technologies: Hadoop, Spark.
  • Distributed databases: Hive, Impala, Redis.
  • Statistical analysis, variable selection, dimensionality reduction.

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Published 2 weeks ago

Expires 1 month from now

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Banque Misr

Le Caire