Senior Data Science Engineer

Key Skills

Aartificial intelligence, Analytical and Problem solving, Apache Airflow, Apache Hadoop, Apache Kafka, AWS, Big Data Technology, Data Analytics, Data science techniques, Database, Design, Effective communication skills, Elasticsearch, Generative AI, Keras software library, Leadership Skill, Machine learning techniques, MySQL, Natural Language Processing (NLP), NoSQL, NumPy, Oracle, Pandas, PostgreSQL, Python Programming, PyTorch, R Programming, SPARK Programming, SQL, Tableau, Teamwork, TensorFlow, Web APIs

Job Description

• Develop, implement, and maintain advanced data analysis algorithms and models to solve complex problems and generate valuable insights from our data.
• Design, develop, and train machine learning models, including generative AI models, to meet business needs.
• Collaborate with cross-functional teams to understand the business needs, and formulate and complete end-to-end analysis that includes data gathering, processing, analysis, and presentation of the results.
• Leverage big data technologies to design, develop, and maintain scalable data-driven features and models.
• Maintain data science and engineering models and iteratively scale up from proof-of-concept to enterprise-wide deployment.
• Provide subject matter expertise on data science methodology and data analytics.
• Work with data engineers to construct, preprocess, and clean datasets for data science projects.
• Keep abreast of the latest developments in the field of data science, machine learning, and AI and explore and experiment with new technologies, techniques, and algorithms.
• Provide mentorship and guidance to junior data scientists and engineers on the team.
• Document and communicate technical processes, model architectures, and development and deployment procedures clearly and effectively to both technical and non-technical stakeholders.
• Lead and contribute to projects involving the conceptualization and development of new data science products or features.
• Work closely with data engineers and other technical staff to ensure the proper implementation and integration of the models and handle any preprocessing, cleaning, or transformation of data as needed.
• Collaborate closely with cross-functional teams to integrate, test, and deploy machine learning solutions into the company's product offerings.
• Data mining or extracting usable data from valuable data sources
• Facility with big data analytics tools and distributed systems, Apache Spark, Kakfa, Airflow etc...
• AI/ML continuous delivery and automation pipeline

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.

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