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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - SDM - **Company:** Aceolution Inc - **Location:** Franklin, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Software Quality, Continuous Delivery, Continuous Integration, Data Security, Data Warehousing, Decision Support Systems, DevOps, Distributed Systems, R (Programming Language), Python (Programming Language), Knowledge-Based Systems, Machine Learning, Natural Language Processing, Tensorflow, Software Safety, Google Cloud, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Scikit Learn, Information Technology, HuggingFace, Data Analytics, Machine Learning Operations, Evaluation of Large Language Models - **Published:** October 9, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/ppzzgw742g ## About the Role We are seeking a highly skilled and experienced Data Scientist - SDM to join our growing team. The ideal candidate will be a data-driven innovator with deep expertise in machine learning, advanced analytics, statistical modeling, and Generative AI technologies. This role requires a strategic thinker capable of transforming complex data into actionable insights and scalable AI-powered solutions that drive business growth., * Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Data Science, Physics, Economics, Engineering, or a related quantitative discipline. * 10+ years of progressive experience in Data Science, Machine Learning, or AI-related roles. * Proven experience delivering enterprise-scale machine learning and AI solutions in production environments. * Strong proficiency in Python and/or R programming. * Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, or similar technologies. * Deep understanding of statistical modeling, predictive analytics, experimentation, A/B testing, and causal inference. * Experience working with large-scale datasets, distributed computing platforms, and data warehousing environments. * Strong problem-solving, analytical, and critical-thinking skills. * Excellent communication and presentation skills with the ability to explain complex technical concepts to diverse audiences. * Demonstrated leadership experience in mentoring teams and driving technical initiatives. Preferred Qualifications * Experience designing and deploying Generative AI, LLM, and RAG-based applications. * Experience with cloud platforms such as Google Cloud Platform (GCP), Vertex AI, AWS, or Azure. * Strong understanding of MLOps frameworks, CI/CD pipelines, and model lifecycle management. * Experience with distributed computing technologies such as Apache Spark. * Prior experience working within Google or PLX environments. * Knowledge of AI governance, Responsible AI, AI safety, data privacy, and data security best practices. * Experience with vector databases, embeddings, prompt engineering, and LLM evaluation frameworks. Key Competencies * Data Science Leadership * Machine Learning * Generative AI & LLMs * Retrieval-Augmented Generation (RAG) * Natural Language Processing (NLP) * Statistical Modeling * Predictive & Prescriptive Analytics * MLOps & Model Deployment * AI Strategy & Innovation * Data-Driven Decision Making * Technical Leadership & Mentoring * Stakeholder Management * Executive Communication * Problem Solving & Critical Thinking ## Description Machine Learning & Model Development * Design, develop, and optimize advanced machine learning models, predictive algorithms, and statistical frameworks. * Lead the end-to-end data science lifecycle, including data collection, exploratory data analysis, feature engineering, model training, deployment, and monitoring. * Build scalable and production-ready AI solutions to address complex business challenges. * Evaluate model performance and continuously improve accuracy, reliability, and scalability. * Identify and mitigate model biases, data quality issues, and performance bottlenecks. Generative AI & Large Language Models (LLMs) * Lead the design, development, and deployment of Generative AI applications and AI-driven products. * Fine-tune and optimize Large Language Models (LLMs) for domain-specific business use cases. * Architect and implement Retrieval-Augmented Generation (RAG) frameworks and intelligent knowledge systems. * Develop advanced Natural Language Processing (NLP) solutions for automation, content generation, search, and decision support. * Evaluate emerging AI technologies and recommend innovative approaches to enhance business capabilities. Data Science Strategy & Business Impact * Partner with business leaders to identify high-value AI and analytics opportunities. * Translate complex analytical findings into clear, actionable recommendations for stakeholders across Product, Marketing, Operations, Finance, and Leadership teams. * Define key performance indicators (KPIs) and success metrics for AI initiatives. * Drive data-driven decision-making through predictive and prescriptive analytics. * Ensure alignment between business objectives and technical solution design. Leadership & Mentorship * Mentor, coach, and guide data scientists, machine learning engineers, and analytics professionals. * Establish best practices for experimentation, model development, code quality, and AI governance. * Conduct technical reviews and provide architectural guidance for AI and ML projects. * Foster a culture of innovation, collaboration, and continuous learning. * Support talent development and contribute to building high-performing AI teams. MLOps & Productionization * Collaborate with Engineering and DevOps teams to deploy, monitor, and maintain machine learning models in production environments. * Improve and scale MLOps workflows, model monitoring frameworks, and deployment pipelines. * Drive automation of model lifecycle management and continuous integration/continuous deployment (CI/CD) processes. * Ensure reliability, security, and scalability of deployed AI systems. Communication & Stakeholder Management * Present complex technical concepts, model outcomes, and business recommendations to executive leadership and non-technical stakeholders. * Create technical documentation, model evaluation reports, and solution architecture documents. * Act as a trusted advisor and subject matter expert in Data Science, Machine Learning, Generative AI, and Responsible AI practices. * Collaborate with cross-functional teams to drive successful project delivery. Research & Innovation * Stay current with emerging trends, academic research, and advancements in Machine Learning, Generative AI, NLP, and Data Science. * Evaluate and experiment with new technologies, frameworks, and methodologies. * Promote innovation and adoption of best-in-class AI solutions across the organization.