Job Overview

data science + gen ai

major duties & responsibilities:

• work with business stakeholders and cross-functional smes to deeply understand business context and key business questions.
• create proof of concepts (pocs) / minimum viable products (mvps), then guide them through to production deployment and operationalization of projects.
• influence machine learning strategy for digital programs and projects.
• make solution recommendations that appropriately balance speed to market and analytical soundness.
• explore design options to assess efficiency and impact, and develop approaches to improve robustness and rigor.
• develop analytical/modeling solutions using a variety of commercial and open-source tools (e.g., python, r, tensorflow).
• formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
• design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
• create algorithms to extract information from large, multiparametric data sets.
• deploy algorithms to production to identify actionable insights from large databases.
• compare results from various methodologies and recommend optimal techniques.
• design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
• develop and embed automated processes for predictive model validation, deployment, and implementation.
• work on multiple pillars of ai, including cognitive engineering, conversational bots, and data science.
• ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment.
• lead discussions at peer reviews and use interpersonal skills to positively influence decision making.
• provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices.
• facilitate cross-geography sharing of new ideas, learnings, and best practices.

required qualifications:

• educational requirement: bachelor of science or bachelor of engineering (at a minimum).
• experience: 4+ years of work experience as a data scientist.

skills:

• a combination of business focus, strong analytical and problem-solving skills, and programming knowledge to quickly cycle hypotheses through the discovery phase of a project.
• advanced skills with statistical/programming software (e.g., r, python) and data querying languages (e.g., sql, hadoop/hive, scala).
• good hands-on skills in both feature engineering and hyperparameter optimization.
• experience producing high-quality code, tests, and documentation.
• experience with microsoft azure or aws data management tools such as azure data factory, data lake, azure ml, synapse, databricks.
• understanding of descriptive and exploratory statistics, predictive modeling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and deep learning methodologies.
• proficiency in statistical concepts and machine learning algorithms.
• good knowledge of agile principles and processes.
• ability to lead, manage, build, and deliver customer business results through data scientists or professional services teams.
• ability to share ideas compellingly, summarize and communicate data analysis assumptions and results.
• self-motivated and a proactive problem solver who can work independently and in teams.