Full-Time Senior Data Scientist

In this role as a Senior Data Scientist at Docplanner, your main focus will be unlocking actionable insights for our senior leadership and operations, with particular emphasis on Sales excellence. You will take ownership of our high-impact Decision Science initiatives, leveraging our large-scale data sources and ML infrastructure, while collaborating closely with cross-functional team members, including business stakeholders and data engineers. 

You will be part of a team of passionate data professionals in our Global Data department, responsible for overseeing data and analytics initiatives across Docplanner’s international operations. This role requires the right blend of technical expertise and business acumen: we highly value pragmatic, simple and elegant solutions with proven impact. The ideal candidate has already experience in a similar role and is comfortable participating in complex scale-up organizations within the SaaS industry. 

How would you be impacting our mission?

• Lead the complete model development lifecycle, implementing best practices for consistent testing and efficient software delivery.
• Create, deploy, and fine-tune predictive models and machine learning algorithms for specific use cases, including developing ML models to interpret data, recognize patterns, and forecast outcomes.
• Craft rigorous experiments and deliver insights to answer empirical questions.
• Collaborate with data engineering and infrastructure teams to maintain and enhance a centralized data platform.
• Participate in interdisciplinary projects at the intersection of business and technology, utilizing opportunities to develop innovative solutions that provide substantial value to customers on a large scale.
• Document workflows, model specifics, and software solutions to facilitate knowledge sharing and ensure reproducibility.

What will help you thrive?

• 5+ years of relevant experience in data, working with state-of-the-art technologies.
• Good understanding of statistical models (hypothesis testing, regression analysis, etc.) and time series analysis
• Expertise in ML-based methodologies, both supervised and unsupervised (regression, tree models, etc.), and model evaluation techniques.
• Hands-on experience in model development, deployment, and optimization using libraries and frameworks like NumPy, Pandas, Scikit-learn, PyTorch, and Keras.
• Experience working with Data Warehouses such as Redshift, BigQuery, and Snowflake, and familiarity with version control practices using Git.
• Understanding of model development lifecycle, MLOps, containerization and orchestration: Docker, Kubernetes, MLFlow, etc.
• Excellent communication skills and stakeholder management, with the ability to convey complex methodologies and data insights clearly to both technical and non-technical audiences.
• Strong ability to translate business needs into technical requirements, including feature engineering and model fine-tuning, with a pragmatic approach to problem-solving.
• Agile mindset with the capability to experiment, test hypotheses, and adapt to a constantly evolving work environment, alongside a commitment to continuous learning and big-picture thinking.
• Growth mindset: While we don't expect you to check every box, a passion for learning and self-improvement is highly valued. 
• Which skills are ‘bonus points’?
• Databricks, Spark, and other big data technologies.
• Neural Networks, NLP, Reinforced Learning.
• Tableau, PowerBI, or other BI tools.
• Experience working in an agile environment, collaborating with cross-functional teams.