Data Scientist

About the role

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

Hold a degree as stated in the listed requirements.

Demonstrate strong hands-on experience building and deploying data science systems from prototype to production in a fast-paced environment.

Apply advanced predictive modeling skills, including traditional ML algorithms, deep learning frameworks, and complex time-series forecasting.

Write high-quality production-grade Python, using practices such as testing, clean code, code review, and CI/CD.

Leverage data and pipeline expertise by optimizing data models and access patterns in modern data warehouses (Snowflake, Databricks) with SQL and transformation tools like dbt.

Operate effectively with cloud and data infrastructure on AWS, GCP, or Azure, and handle columnar data formats such as Parquet.

Apply MLOps experience with frameworks for training, tracking, deploying, and monitoring models in production (for example, MLflow, Optuna, Prefect, or Airflow).

Nice to have

Show background in supply chain forecasting, enterprise AI, or retail analytics.

Possess an M.S. or Ph.D. in Computer Science, Artificial Intelligence, Statistics, Data Science, or a related quantitative field.

Skills & tools

Data Science, Machine Learning, Deep Learning, Time-series forecasting, Python, Pandas, dbt, Snowflake, AWS, GCP, Azure, MLflow, Optuna, MLOps.

Practical notes

This role is based in London, United Kingdom. The position requires hands-on work with client data during onboarding and customization engagements.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data Scientist roles typically combine modeling, data engineering, and deployment. Tools such as Python, Snowflake, and MLOps platforms support scalable analytics. Demand sensing and cold-start forecasting are common problems in retail analytics. MLOps practices help move models from experimentation to reliable production. Roles in analytics often require close collaboration with product, engineering, and customer success teams.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Headquarters in Miami, Florida hosts a company focused on business planning software. Subscription sales fund access to cloud tools. Teams receive data intended to guide choices. Purpose centers on planning support and decision information. Services target organizations seeking structured approaches to forecasts and collaboration. Online platforms deliver content. Commitment stays directed toward clarity in plans. Continuous updates provide fresh inputs. Clients rely on consistent tools. Overall direction aims to simplify complex planning demands through connected systems.