Senior Data Scientist

Docplanner

6d•Remote

About The Position

As a Senior Data Scientist at Docplanner, you will help transform complex data into actionable insights and scalable machine learning solutions that directly impact business growth and operational excellence. You will work on high-impact initiatives across Business, Operations, and Product areas, partnering closely with business stakeholders, engineers, and analytics teams. You will own data science and BI projects end-to-end - from problem definition and experimentation to model deployment and monitoring - using large-scale datasets and ML infrastructure. We are looking for someone who combines strong technical expertise with business understanding and a pragmatic, impact-driven mindset. This role is part of our Global Data department, supporting data and AI initiatives across Docplanner’s international operations.

Requirements

5+ years of experience in Data Science, Machine Learning, or Advanced Analytics roles.
Strong Python skills and hands-on experience with libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch.
Solid understanding of statistics, experimentation, machine learning, and model evaluation techniques.
Experience working with large datasets, SQL, and data warehouses such as Redshift, BigQuery, or Snowflake.
Familiarity with MLOps and modern development practices (Git, Docker, MLflow, CI/CD).
Experience using AI-powered tools or AI agents to improve efficiency and automate repetitive tasks.
Strong communication skills and ability to translate business needs into practical technical solutions.
Comfortable working in agile, fast-paced, and cross-functional environments.

Nice To Haves

Experience with Airflow, k8s, Jupyter Notebook, Athena, or other big data technologies.
Experience with NLP, LLMs, or AI agent workflows.
Familiarity with BI and visualization tools such as Tableau or Superset.
Previous experience in SaaS or high-growth tech environments.

Responsibilities

Own and deliver end-to-end data science and BI projects, from exploration and experimentation to deployment in production.
Build predictive models and machine learning solutions that support business decision-making and operational efficiency.
Analyze large datasets to uncover actionable insights, trends, and growth opportunities.
Design and evaluate experiments and A/B tests to measure impact and validate hypotheses.
Collaborate with cross-functional teams across Product, Sales, Marketing, and Engineering.
Improve and maintain scalable data pipelines and ML workflows together with Data & ML engineering teams.
Use modern AI tools, copilots, and AI agents to automate workflows, accelerate experimentation, and improve productivity.
Contribute to best practices in model development, testing, deployment, and documentation.