About talentflow
talentflow is a forward‑thinking leader in the retail technology space, delivering cutting‑edge solutions that empower merchants to make data‑driven decisions at scale. Our mission is to transform the way retail operations function by harnessing the power of artificial intelligence, advanced statistics, and automation. With a global footprint and a culture that celebrates curiosity, collaboration, and continuous learning, talentflow offers a dynamic environment where innovative ideas become real‑world impact.
Position Overview
We are seeking a highly skilled Part‑Time Data Science & Machine Learning Engineer to join our remote team. In this role, you will lead the design, development, and deployment of sophisticated forecasting algorithms and data‑pipeline solutions that solve complex business challenges for our retail partners. You will work closely with cross‑functional AI teams, data engineers, product managers, and business stakeholders to turn massive, high‑dimensional data sets into actionable insights and scalable machine‑learning products.
Key Responsibilities
- Algorithm Development: Design, prototype, and refine long‑term forecasting models that address retail‑specific pain points such as inventory optimization, demand planning, and price elasticity.
- Data Pipeline Engineering: Build robust ETL pipelines to ingest, clean, and transform terabytes of structured and unstructured retail data for downstream analytics.
- Feature Engineering & Model Training: Conduct extensive feature extraction, feature selection, and feature construction to improve model performance on large‑scale datasets.
- Model Validation & Deployment: Implement rigorous validation frameworks, monitor model drift, and productionize models using containerized services and cloud‑native platforms.
- Collaboration & Communication: Translate complex technical findings into clear business narratives for senior leadership, product owners, and non‑technical stakeholders.
- Research & Innovation: Stay abreast of emerging techniques in deep learning, probabilistic modeling, and optimization, and evaluate their applicability to retail use cases.
- Mentorship: Guide junior data scientists and engineers, fostering a culture of knowledge sharing and best‑practice adherence.
- Performance Optimization: Continuously monitor and tune model performance, identifying opportunities for algorithmic improvements and cost reductions.
Essential Qualifications
- Education: Minimum of a Master’s degree in Mathematics, Statistics, Computer Science, or a closely related quantitative field.
- Experience: At least 7 years of professional experience as a data scientist or machine‑learning scientist, with a proven track record of delivering end‑to‑end ML solutions (regression, clustering, forecasting, etc.).
- Technical Proficiency: Advanced expertise in Python, SQL, R, and SAS; hands‑on experience with big‑data technologies such as Hadoop, Hive, Spark, and distributed computing frameworks.
- Statistical & Mathematical Foundations: Deep understanding of probability theory, statistical inference, linear programming, and optimization techniques.
- Model Lifecycle Management: Demonstrated ability to take models from research through production, including version control, testing, and monitoring.
- Problem‑Solving Acumen: Strong analytical mindset with the ability to dissect complex business problems, formulate hypotheses, and deliver data‑driven solutions.
- Communication Skills: Excellent written and verbal communication skills, capable of presenting technical concepts to diverse audiences.
- Remote Work Capability: Ability to work independently from any location within the United States, with a reliable internet connection and a suitable home office setup.
Preferred Qualifications
- Ph.D. in Mathematics, Statistics, or a related quantitative discipline.
- 4+ years of experience specifically in large‑scale retail analytics or e‑commerce environments.
- Proficiency with additional programming languages such as Java, Scala, C, or JavaScript.
- Experience with deep learning frameworks (TensorFlow, PyTorch) and natural language processing pipelines.
- Familiarity with cloud platforms (AWS, GCP, Azure) and container orchestration tools (Docker, Kubernetes).
- Track record of publishing research findings or contributing to open‑source ML projects.
Core Skills & Competencies
- Data Exploration & Visualization: Ability to perform exploratory data analysis, generate insightful visualizations, and communicate findings effectively.
- Statistical Modeling: Mastery of time‑series analysis, Bayesian methods, and multivariate regression techniques.
- Machine Learning Engineering: Expertise in building scalable pipelines, feature stores, and model serving architectures.
- Big Data Processing: Proficiency with Spark, Hadoop, Hive, and related ecosystems for handling massive data volumes.
- Software Engineering Practices: Commitment to clean code, unit testing, code reviews, and version control (Git).
- Collaboration: Strong team player who thrives in a multicultural, interdisciplinary environment.
- Adaptability: Comfortable navigating ambiguous requirements and rapidly evolving project scopes.
Career Growth & Learning Opportunities
talentflow invests heavily in the professional development of its talent. As a part‑time member of our AI team, you will have access to:
- Mentorship from senior data scientists and industry veterans.
- Annual learning stipends for conferences, certifications, and online courses.
- Opportunities to lead high‑visibility projects that directly influence product strategy.
- Cross‑functional rotations that broaden exposure to product management, engineering, and business analytics.
- Publication support for research papers and internal knowledge‑sharing platforms.
Work Environment & Culture at talentflow
Our remote‑first culture is built on trust, autonomy, and results‑orientation. We celebrate diversity, encourage curiosity, and foster an inclusive environment where every voice is heard. Key cultural pillars include:
- Innovation First: We allocate dedicated time for experimentation and encourage creative problem‑solving.
- Collaboration Across Borders: Virtual “coffee chats,” global hackathons, and regular team‑wide syncs keep us connected.
- Work‑Life Harmony: Flexible scheduling allows you to balance professional commitments with personal priorities.
- Transparency: Open communication channels and regular leadership updates keep everyone aligned with the company’s vision.
Compensation, Perks & Benefits
talentflow offers a competitive, performance‑based compensation package that reflects the expertise you bring to the role. While exact figures are tailored to experience, you can expect:
- Hourly rates that are above market average for part‑time data science talent.
- Comprehensive health, dental, and vision coverage (available to eligible part‑time employees).
- Retirement savings options with employer matching contributions.
- Generous paid time off and holiday calendars.
- Remote‑work stipend for home‑office equipment and high‑speed internet.
- Access to a curated library of technical resources, webinars, and internal training modules.
How to Apply
If you are passionate about turning massive retail data sets into predictive insights, thrive in a remote, collaborative environment, and meet the qualifications outlined above, we want to hear from you. To submit your application, please click the link below and provide your resume, a cover letter highlighting relevant experience, and any portfolio or GitHub links that demonstrate your work.
Join talentflow and Shape the Future of Retail Analytics
At talentflow, your expertise will directly influence the next generation of retail intelligence tools used by thousands of merchants worldwide. This is more than a part‑time role; it is an opportunity to leave a lasting imprint on an industry in transformation. Take the next step in your career—apply today and become a catalyst for change.
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