Softcrylic - Data Scientist - Digital Intelligence

Senior Data Scientist (Digital Intelligence)

Overview:

- We are seeking a highly experienced and versatile Senior Data Scientist to join our team. This role focuses on leveraging advanced statistical methods, machine learning, and emerging Large Language Model (LLM) technologies to derive deep insights from large datasets, particularly within the digital domain.

- You will be responsible for end-to-end data science projects, from initial strategy and complex troubleshooting in large data pipelines to the final implementation and presentation of actionable intelligence. This is an excellent opportunity for an individual with extensive statistical training and a passion for building next-generation AI agents.

Key Responsibilities:

- Problem Solving & Project Ownership: Take ownership of and drive independent problem solving on small to medium-scale projects, defining objectives, selecting methodologies, and delivering high-impact solutions.

- Advanced Modeling: Apply knowledge of advanced statistical methods and machine learning to develop predictive, prescriptive, and explanatory models. LLM & AI Implementation: Design and implement solutions utilizing Large Language Models (LLMs), including hands-on experience with Retrieval-Augmented Generation (RAG)-based AI agents to enhance data retrieval and analysis capabilities.

- Digital Analytics: Lead analyses focused on digital behavior analytics and UX analyses, translating raw user interaction data into clear, actionable product or marketing recommendations. Data Engineering & Cloud: Work with large datasets in cloud environments (primarily AWS), ensuring data quality and model readiness within complex, large data pipelines.

- Communication & Collaboration: Act as a strong technical communicator who is comfortable troubleshooting very complex data problems and effectively communicating results to a non-technical audience and stakeholders.

- Requirements & Presentation: Lead efforts in gathering requirements from business units and presenting results and model trade-offs clearly to decision-makers. Performance Metrics: Maintain deep familiarity with employing different statistical performance measures and clearly understanding the benefits/trade-offs of each measure for model evaluation.

- Technical Skills and Requirements Statistical Foundation: Extensive statistical training and practical experience applying statistical inference and modeling techniques. Programming & Querying: Strong SQL and Python skills are mandatory for data manipulation, cleaning, and model development.

- Machine Learning: Hands-on experience with deploying and productionizing machine learning models. LLM & RAG: Hands-on experience with Large Language Models (LLMs) implementation and building Retrieval-Augmented Generation (RAG)-based AI agents. Cloud Experience: Practical experience working with large data sets and AWS machine learning services (e.g., S3, Glue, Athena).

- Domain Experience (Preferable): Experience in digital marketing or a related field is preferable, providing context for digital behavior and campaign analysis. UX/Behavioral Data: Proven experience with UX analyses and digital behavior analytics (e.g., clickstream data, A/B testing).

- Nice to Have Skills Data Orchestration: Experience with Airflow (or similar tools like Dagster or Prefect) for building reliable data/ML pipelines. Cloud ML Tools: Familiarity with advanced cloud ML platforms such as Google Auto ML and AWS SageMaker. Visualization: Proficiency in data visualization tools like Tableau or Power BI. Multi-Cloud: Familiarity with Azure services is a plus.