Principal Data Scientist

Thoughtworks•Chicago, IL

23h•$182,000 - $320,000

About The Position

Data scientists at Thoughtworks are strategic leaders who spearhead data-driven initiatives, tackle complex business challenges and uncover transformative insights. They possess a deep understanding of a client's business ecosystem and partner with executives to align technology strategies with business objectives. By contextualizing emerging trends and Thoughtworks' exploration, they expand the impact of data science within the client organization. Drawing upon their profound expertise in statistics, modeling, machine learning algorithms and data mining methodologies, our data scientists integrate this knowledge with a comprehensive understanding of related domains to deliver tangible business value. They devise strategies, execute plans, operationalize solutions and articulate the business outcomes achieved. Effective collaboration is paramount, as they adeptly convey their discoveries to both technical and non-technical stakeholders. They stay abreast of industry advancements, ensure data quality and security, and provide mentorship to junior team members. As a data scientist at Thoughtworks, you'll leverage your deep technical knowledge to solve complex business problems, making a significant impact on client success.

Requirements

Experience in the Retail and Customer Analytics industry.
Experience with defining strategy/vision for the data science function, aligning it with the overall business strategy.
Experience in leading the analysis and modeling of different datasets and overseeing an inception to deploying and productionizing the models.
Experience with statistical modeling, machine learning, deep learning, optimization and other data science techniques to implement end-to-end data science projects.
Understanding of the specialized areas of AI and possess a speciality with at least one of them (i.e.: NLP/computer vision/Generative AI, etc)tc).
Experience with R, Python or equivalent statistical/data analysis tools and can write production-ready code that is easy to evolve and test.
Gather and preprocess large volumes of structured and unstructured data from various sources, ensuring data quality and integrity.
Develop and implement machine learning models, algorithms and predictive analytics to solve specific business problems or improve existing processes.
Evaluate model performance using appropriate metrics and validate models to ensure robustness and reliability.
Interpret and effectively communicate findings and insights to non-technical stakeholders through reports, presentations and visualizations.
Understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way.
Resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives.
Do not shy away from risks or conflicts, instead take them on and skillfully manage them.
Eager to coach, mentor and motivate others and aspire to influence teammates to take positive action and accountability for their work.
Enjoy influencing others and always advocate for technical excellence while being open to change when needed.
Proven leader with a track record of encouraging teammates in their professional development and relationships.
Cultivating strong partnerships comes naturally; understand the importance of relationship building and how it can bring new opportunities to our business.

Responsibilities

Establish a clear vision and direction for the AI paradigm/practices, aligning it with the overall business strategy.
Collaborate with executive level stakeholders to understand their strategic objectives and identify opportunities to leverage data and data quality for enabling AI and machine learning.
Be responsible for the overall AI system design, problem framing and governance, identifying and managing risks and issues.
Identify and prioritize data science projects that align with business objectives and ensure that projects are completed on time and within budget.
Design experiments for the team to rapidly test ideas and assumptions, and identify future courses of action.
Get involved with data science delivery whenever needed.
Communicate technical findings and insights to stakeholders in a clear, easy and concise way, keeping the stakeholders in mind.
Build and drive the growth of the data science community along with promoting a culture of data literacy within the organization.
Address ethical concerns and adhere to ethical guidelines using FATTER AI (fairness, accountability, transparency, trustworthiness, explainability, responsibility).
Work with CD4ML practices and data versioning tools.