Data scientist

Role & responsibilities

Data Preparation:

Clean, pre-process, and engineer features from diverse data sources, such as website interactions, engagement metrics, and demographic information, to make them usable by ML models.

Model Development:

Build, train, and evaluate machine learning models, such as logistic regression, to predict lead conversion probability based on historical sales data.

Model Deployment & Maintenance:

Integrate ML models into existing CRM and sales platforms, ensuring they are continuously updated and performing optimally

Performance Analysis:

Monitor model performance, analyze results, and iterate on models to improve accuracy and effectiveness over time.

Prompt Engineering:

Crafting effective prompts to elicit desired responses from LLMs and leveraging techniques like Retrieval Augmented Generation (RAG).

Model Evaluation:

Developing and implementing evaluation frameworks to assess the performance of LLMs using both quantitative and qualitative metrics.

Programming Skills:

Proficiency in programming languages used for data science, such as Python, including relevant libraries like scikit-learn.

Preferred candidate profile

Machine Learning with Lead Scoring Techniques, Prompt Engineering, Python.

Stakeholder Management, Presenting findings to leadership team