Data Scientist

Overview

At SAAL.ai, the role of a Data Scientist involves employing machine learning and deep learning techniques to address tangible business challenges using both numerical and textual data analytics. Candidates will be engaged throughout the entire data science journey, starting from data gathering and feature extraction to model creation and deployment. Close collaboration with engineering, product development, and DevOps teams is essential to deliver AI solutions centered on customer needs.

Responsibilities

• Create and implement robust machine learning and deep learning models for diverse analytics scenarios.
• Evaluate the practical applicability and limitations of AI models within products, selecting suitable modeling techniques to ensure relevance to business goals and technical stability.
• Collect data from multiple sources, perform thorough data exploration and cleaning on both structured and unstructured datasets, engineer meaningful features, test various algorithms and architectures, and iteratively enhance model performance.
• Discover valuable data sources and automate the process of data gathering and preprocessing when feasible.
• Develop, optimize, and sustain machine learning pipelines; integrate algorithms and components into the platform; and contribute new models to the platform's marketplace.
• Work closely with engineering and DevOps teams to facilitate model deployment and ensure smooth operationalization.
• Engage in applied research initiatives, suggest data-driven solutions to business problems, and collaborate with product, support, and client teams to successfully launch AI solutions through trials and general availability.

Required Skills

• Strong theoretical and practical knowledge of machine learning and deep learning, with hands-on experience building pipelines for both structured and unstructured data.
• Proficiency with deep learning frameworks like TensorFlow and PyTorch, and machine learning libraries such as scikit-learn.
• Expertise in Python programming along with familiarity with Java or R.
• Experience in developing and exposing models through REST APIs and containerizing applications using Docker.
• Adherence to strong coding standards, including effective use of data structures and producing clean, maintainable code.
• Applied experience in natural language processing, employing transformer-based models and libraries such as Hugging Face, spaCy, or Gensim; knowledge of SQL, Pandas, Apache Spark, and handling of large-scale data processing.
• Familiarity with machine learning lifecycle tools like MLflow.
• Excellent analytical and communication skills with the ability to clearly explain technical concepts to both internal and external audiences.
• Demonstrated ability to collaborate effectively within cross-functional teams.

Preferred Skills

• Experience working with large language models (LLMs), including deployment in offline environments.
• Exposure to cloud platforms such as AWS, Google Cloud Platform, or Azure.
• Experience with data visualization tools and collaborative environments like Jupyter Notebooks and Git workflows.
• Background or domain expertise with education or finance-related datasets is advantageous.