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Senior Data Scientist
4 months ago
About the Role :
We are searching for a talented and experienced Senior Data Scientist to join our team and play a key role in leveraging the power of machine learning (ML) and artificial intelligence (AI) to extract valuable insights from data.
In this role, you will be responsible for the entire data science lifecycle, from data acquisition and wrangling to model development, deployment, and communication of results.
Responsibilities :
- Design and execute machine learning pipelines for various tasks including supervised learning, unsupervised learning, reinforcement learning, natural language processing (NLP) / natural language understanding (NLU) / large language models (LLM), and computer vision (CNN).
- Work with big data platforms like Spark and Databricks to process and analyze large datasets.
- Develop and implement machine learning models using frameworks like TensorFlow, Keras, MXNet, or PyTorch and libraries like scikit-learn, numpy, pandas.
- Perform exploratory data analysis (EDA), data cleaning, feature engineering, and model selection.
- Continuously monitor and evaluate model performance, and iterate to improve results.
- Effectively communicate complex technical concepts to both technical and non-technical audiences through presentations and reports.
- Stay up-to-date on the latest advancements in data science and machine learning.
Qualifications :
Required :
- 7+ years of experience in researching, developing, and delivering impactful data-driven solutions using machine learning.
- Deep understanding of supervised, unsupervised, reinforcement learning, NLP/NLU/LLM techniques, and CNNs.
- Proficiency in exploratory data analysis, data profiling, and feature engineering on large datasets (structured and unstructured).
- In-depth knowledge and hands-on experience with SQL, Python, R, or similar programming languages.
- Excellent written and verbal communication and consulting skills with the ability to explain complex findings and data insights to a diverse audience.
Preferred :
- Deep knowledge of probability, statistics, and machine learning algorithms, with the ability to determine their appropriate application.
- Familiarity with big data platforms (Spark, Databricks), machine learning frameworks (TensorFlow, Keras, MXNet, PyTorch), and libraries (scikit-learn, numpy, pandas).
- Experience with Azure or similar cloud platforms.
- Experience presenting to technical and non-technical audiences.
- A history of publications or presentations at conferences is a plus.
Skills :
- Machine learning frameworks and libraries (TensorFlow, Keras, MXNet, PyTorch, scikit-learn)
- Big data platforms (Spark, Databricks)
- Programming languages (Python, R, SQL)
- Data science (exploratory data analysis, data cleaning, feature engineering)
- Cloud platforms (Azure)
- Deep learning (CNNs)
- Natural Language Processing (NLP) / Natural Language Understanding (NLU) / Large Language Models (LLMs)
- Reinforcement Learning
- Statistics and probability
- Communication and storytelling
