Data Scientist Lead Consultant

Senior Data Scientist

100% Remote Work

Flexible Schedule

12 months

Overview: As a Senior Data Scientist, you will leverage your expertise in quantitative methods, including machine learning, to solve intricate problems and empower stakeholders with data-driven insights. Your role will involve processing vast amounts of data, extracting meaningful patterns, and developing solutions to address pressing business challenges.

Role Specifics:

Engage with internal stakeholders to ensure analytic solutions align with their needs and deliver tangible results.

Translate real-world business challenges into analytical frameworks.

Utilize a diverse skill set, including data mining, machine learning, statistics, visualization, and more.

Collaborate with teams to gather, refine, and harmonize data, ensuring its readiness for analysis.

Simplify complex results, avoiding excessive technical jargon, and employ tools and cloud services like Python, R, SQL, and AWS.

Qualifications:

AWSCloud Practitioners Certificate or the ability to obtain it during onboarding.

At least 5 years of experience in data science, machine learning, or related fields.

Proficiency in programming (Python or R) and familiarity with essential data analysis tools and libraries.

Demonstrated expertise in machine learning and AI techniques.

Exceptional problem-solving abilities and a knack for innovative solutions.

Stellar communication skills, with a history of conveying complex ideas effectively.

A proven record of delivering data science projects efficiently.

Key Responsibilities:

Data Analysis & Model Development: Craft machine learning models and products, ensuring their technical robustness and relevance to business users.

Collaboration: Partner with cross-functional teams to discern business requirements and pinpoint opportunities for advanced analytics, especially in demand forecasting.

Solution Deployment: Design, develop, and roll out end-to-end machine learning solutions to address multifaceted business issues.

Tool & Framework Selection: Choose the right tools and frameworks for data science projects, particularly those related to demand forecasting.

Quality Assurance: Guarantee the precision and reliability of data science outputs through meticulous validation, testing, and performance tracking.

Communication: Articulate intricate data science concepts and findings to a varied audience, ensuring clarity for both technical and non-technical stakeholders.

Continuous Learning: Stay updated with the latest in data science, machine learning, and AI. Proactively seek opportunities to incorporate novel techniques in demand forecasting.

Team Culture: Promote a culture of continuous learning, innovation, and collaboration within the team and the broader organization.