Senior Data Scientist - Portfolio

Jobgether; United States; Remote; USD 120,000 - 190,000; Full time

Benefits offered by this job

Performance-based bonus

Attendance bonus

Private pension plan

Meal allowance

Health, dental, and life insurance

Discounts on medicines

Wellhub health partnership

Childcare subsidies

Discounts on company products

Clube Ben benefits access

Scholarship opportunities

School materials assistance

Language-learning and training access

Transportation allowance

Additional days off

Casual office environment

Job summary

Jobgether in the United States seeks an experienced data scientist to design and deploy advanced ML models for portfolio optimization, recommendations, and personalization. You will collaborate with data engineers and product teams to translate business goals into deployable solutions.

Responsibilities include building scalable data pipelines on Azure/Databricks, evaluating new methods, and mentoring junior teammates. Strong Python and Spark experience required; attractive benefits apply.

Qualifications

• Bachelor's degree in Mathematics, Statistics, Engineering, CS or related field.
• Experience applying ML in production with measurable impact.
• Strong Python for data analysis and production workflows.
• Experience with Spark/PySpark and cloud platforms (Azure/Databricks).
• Knowledge of model evaluation, validation, and experimentation frameworks.
• Experience with version control, CI/CD, automation, reproducible workflows.
• Ability to design scalable data pipelines and production ML solutions.
• Proficient use of AI coding tools responsibly and securely.
• Excellent communication and collaboration skills.
• Analytical thinking, curiosity, independence, mentoring ability.

Responsibilities

• Design, develop, and deploy ML/DL models for portfolio optimization and recommendations.
• Analyze complex datasets to identify business opportunities and insights.
• Evaluate new ML techniques to solve business challenges.
• Architect and optimize large-scale data pipelines on Azure, Databricks, Spark, PySpark.
• Build reliable pipelines with Python and PySpark, with automation.
• Ensure production-ready models balancing accuracy, stability, and scalability.
• Promote best practices for experimentation, validation, CI/CD, reproducibility.
• Collaborate with data engineers, product managers, and stakeholders.
• Mentor junior data scientists and share knowledge.

Skills

Python programming

Statistical modeling

Experimentation

Communication

Mentoring

Education

Bachelor's degree in quantitative field

Master's degree preferred

PhD advantageous

Tools

Azure

Databricks

Spark

PySpark

CI/CD

Job description

Accountabilities:

• Design, develop, and deploy advanced machine learning and deep learning models for portfolio optimization, recommendations, personalization, promotions, segmentation, data imputation, and insight discovery.
• Analyze complex datasets to uncover business opportunities, generate actionable insights, and support strategic decision-making through robust statistical modeling and predictive analytics.
• Drive innovation by evaluating and adopting emerging machine learning techniques, advanced algorithms, and AI-driven approaches to solve complex business challenges.
• Architect, develop, and optimize large-scale data processing workflows using cloud technologies and distributed computing platforms, including Azure, Databricks, Spark, and PySpark.
• Build and maintain reliable, scalable data pipelines and modeling workflows using Python and PySpark, incorporating automation and reusable components to improve efficiency and reduce operational overhead.
• Ensure models are production-ready, balancing predictive accuracy, stability, explainability, scalability, and real-world business constraints.
• Establish and promote best practices for experimentation, model validation, performance evaluation, version control, CI/CD, and reproducible machine learning workflows.
• Collaborate closely with data engineers, product managers, and business stakeholders to translate business objectives into technical requirements and deliver end-to-end data science solutions.
• Mentor junior and mid-level data scientists, share technical knowledge, and contribute to a culture of experimentation, technical excellence, and continuous learning.

Requirements:

• Bachelor's degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative discipline. A master's degree is preferred, and a PhD is considered an advantage.
• Demonstrated experience applying machine learning and statistical modeling techniques in production environments, with a track record of delivering measurable business impact.
• Strong proficiency in Python for data analysis, model development, experimentation, and production workflows.
• Experience with distributed data processing technologies such as Spark and PySpark, with familiarity with cloud-based platforms such as Azure and Databricks considered a strong advantage.
• Solid understanding of model evaluation, validation methodologies, experimentation frameworks, and statistical rigor, preferably in complex or high-impact decision-making environments.
• Experience with modern machine learning development practices, including version control, CI/CD, automation, and reproducible research and modeling workflows.
• Ability to design and implement scalable data pipelines, optimize computational workflows, and develop reliable machine learning solutions for real-world applications.
• Proficiency in using AI coding agents and developer tools responsibly and effectively to accelerate development while maintaining code quality, security, and production standards.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts, models, and results to both technical and non-technical audiences.
• Strong analytical thinking, curiosity, creativity, and independence, with the ability to challenge assumptions, explore innovative approaches, and reason through complex systems.
• Proven ability to take ownership of technical initiatives, collaborate across multidisciplinary teams, and mentor colleagues while contributing to shared business goals.

Benefits:

• Performance-based bonus, subject to applicable rules.
• Attendance bonus, subject to applicable rules.
• Private pension plan.
• Meal allowance.
• Health, dental, and life insurance.
• Discounts on medicines.
• Wellhub partnership to support health and well-being.
• Childcare subsidies.
• Discounts on company products, subject to applicable rules.
• Access to Clube Ben partnership benefits.
• Scholarship opportunities, subject to applicable rules.
• School materials assistance.
• Access to language-learning and professional training platforms.
• Transportation allowance.
• Additional days off, subject to applicable rules.
• Casual office environment and dress code.