Senior Data science Engineer

Qualifications →

• Bachelor’s or Master’s degree in Computer Science or equivalent
• At least 5 years of experience in a data science role focusing on data analytics and machine learning model development.
• Experience with generative AI and the development and training of machine learning models.
• Proficiency in Python, R, or other programming tools used in data science.
• Proficiency in EDA and statistical analysis of the data.
• Deep understanding of the modules like sklearn, keras, pandas, numpy, nltk, etc…
• Knowledge of machine learning frameworks like TensorFlow, PyTorch, or similar.
• Strong understanding of machine learning algorithms and principles (regression, decision tree/random forest, clustering, PCA, SVM, etc.), and their pros and cons.
• Design rigorous experiments and evaluation methodologies to assess the performance and robustness of machine learning models.
• Experience in designing and implementing robust and scalable pipelines for deploying machine learning models in production environments across various platforms ( on-premises servers, AWS, etc..)
• Experience with SQL and database structures.
• Experience with big data technologies such as Spark, Hadoop, or similar.
• Familiarity with data visualization tools like Tableau, Superset, or similar.
• Strong problem-solving skills with an emphasis on product development.
• Excellent written and verbal communication skills.

Preferred Skills →

• Experience with deep learning and neural networks.
• Experience with natural language processing.
• Demonstrated leadership ability in a team environment.
• Retrieving and comprehending data is crucial for data science engineers. They analyze and extract insights from data, requiring a strong foundation in data retrieval and understanding. Familiarity with common data sources is valuable for efficient access and work across platforms. Examples of such sources include Splunk, Argus, Huron, AWS S3, relational databases (e.g., MySQL, PostgreSQL, Oracle, Microsoft SQL), NoSQL databases (e.g., MongoDB, Cassandra, Redis, Elasticsearch), web APIs, web scraping, sensor data, log files, textual data (e.g., chat logs), and streaming data.