Associate Data Scientist

🔍 What You'll Work On

This role offers exposure to the complete data science workflow, from understanding raw datasets and preparing features to developing models, interpreting results, and supporting their use in business environments. It is particularly suited to candidates who want to build practical experience across traditional data science as well as emerging Generative AI applications.

📊 Data Analysis & Preparation

A significant part of the role involves working with both structured and unstructured data. You will perform exploratory data analysis, data cleaning, feature engineering, and data transformation to prepare datasets for analytical and machine learning applications.

Strong knowledge of Pandas and NumPy will be useful for manipulating datasets, identifying patterns, handling missing or inconsistent information, and preparing reliable inputs for downstream analysis.

Python Pandas NumPy SQL

🤖 Machine Learning Exposure

🧠 Modeling & Prediction

The position requires an understanding of core machine learning approaches across both supervised and unsupervised learning. Depending on the business problem, you may work with regression, classification, clustering, or forecasting techniques.

The expectation is not simply to build models, but to validate and interpret them. Understanding model performance, assumptions, limitations, and the business meaning of the results will be important.

Experience with libraries such as Scikit-learn, statsmodels, TensorFlow, or PyTorch can strengthen a candidate's profile, although these are listed as preferred rather than mandatory technologies.

🧪 Experimentation & Business Decisions

📈 A/B Testing & Experiments

Fortive's Associate Data Scientist role also includes designing and analyzing experiments and A/B tests. This means candidates should understand how controlled experiments can be used to measure the impact of a product, process, or business change.

A basic understanding of statistical significance, probability, experimental design, and causal inference can be valuable when working with these types of problems.

Strong statistical reasoning matters because a useful data science result needs to distinguish meaningful patterns from random variation.

🧩 Generative AI & LLM Applications

✨ Generative AI Exposure

The role specifically includes support for Generative AI and LLM-based analytics use cases. Candidates with exposure to modern large language model ecosystems can therefore bring additional value to the team.

The job description mentions technologies and platforms such as OpenAI, Anthropic Claude, Azure OpenAI, and Gemini. Familiarity with how LLMs can support enterprise analytics, information retrieval, decision support, or other business workflows would be relevant preparation.

This does not replace the core data science requirements. Python, SQL, statistics, machine learning, and analytical problem solving remain central to the position.

📊 Visualization & Reporting

📉 Turning Data Into Insights

Data scientists in this role will also communicate findings through visualizations, dashboards, and reports. The ability to explain technical results to non-technical stakeholders is specifically emphasized.

Candidates may encounter visualization technologies such as Power BI, Tableau, matplotlib, seaborn, or Plotly. Building clear visual explanations of trends, model performance, KPIs, and business metrics can make analytical work considerably more useful to decision-makers.

☁️ Enterprise Technology Exposure

🛠 Tools & Platforms

The role provides an opportunity to work alongside data engineering and ML engineering teams and gain exposure to technologies used in enterprise-scale data environments.

Preferred areas include Azure, AWS, Google Cloud, Databricks, and Spark. Candidates do not necessarily need expertise across every platform, but familiarity with cloud data workflows or distributed data processing can strengthen their preparation.

The position also values software engineering fundamentals, data structures, algorithms, Git, version control, code reviews, and reproducible analytical practices.

AWS Azure Google Cloud Databricks Spark Git

🤝 How You'll Work

🌐 Cross-Functional Collaboration

This is not a role limited to independent model development. The Associate Data Scientist will work with data engineers, ML engineers, and business stakeholders to understand problems and operationalize analytical solutions.

Communication is therefore an important part of the position. Candidates should be comfortable explaining analytical findings, assumptions, methodologies, and limitations to audiences that may not have a technical background.

Documentation is also part of the role. Analyses should clearly communicate the methodology used, assumptions made, results obtained, and relevant conclusions so that work can be reproduced and reviewed.

🎓 Who Can Apply

📚 Educational Background

Fortive is looking for candidates with a bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related field.

The position is aimed at candidates with 0–2 years of experience in data science, analytics, machine learning, or related areas. Academic projects, internships, personal projects, and open-source contributions can therefore be useful when demonstrating practical skills.

Experience Level: 0–2 Years

📝 What Recruiters May Look For

🔎 Hiring Focus

Candidates should be prepared to demonstrate practical understanding rather than only listing technologies on a resume.

Important areas include:

• Python programming and data manipulation

• SQL for extracting and analyzing enterprise data

• Probability and statistics

• Supervised and unsupervised machine learning

• Pandas and NumPy

• Data visualization

• Data structures and algorithms

• Analytical and critical thinking

• Clear communication of technical results

• Git and collaborative development

• Understanding of Generative AI and LLM applications

Candidates with projects demonstrating an end-to-end workflow—from collecting or querying data through analysis, modeling, evaluation, and communication—can present a stronger practical profile.

🚀 Skills That Can Strengthen Your Profile

💡 Useful Preparation Areas

For candidates targeting this type of entry-level data science role, it is useful to have projects that demonstrate more than basic model training.

A strong portfolio could include a predictive modeling project with proper evaluation, a SQL-based analytics project with business KPIs, an experiment or A/B testing analysis, or a Generative AI application connected to a real business use case.

It is also worth understanding how machine learning models move beyond notebooks into production environments. Basic knowledge of MLOps, model deployment, monitoring, cloud platforms, and version control aligns with the preferred skills listed for the position.

Fortive also expects candidates to participate in code reviews, peer reviews, research, and knowledge-sharing activities, making collaborative software development practices relevant to the role.

🌱 What This Role Can Offer

📈 Practical Career Exposure

The position provides exposure to enterprise-scale analytics, machine learning, Generative AI, cloud-native data platforms, and MLOps practices. Candidates can also work alongside experienced data science and technology professionals while contributing to data-driven business initiatives.

For someone beginning a career in data science, the combination of statistical analysis, machine learning, experimentation, SQL, Python, and Generative AI makes this broader than a role focused exclusively on model development.

🔑 Keywords for Resume

🔑 Keywords for Resume

Python • SQL • Data Science • Machine Learning • Statistics • Probability • Pandas • NumPy • Scikit-learn • Regression • Classification • Clustering • Forecasting • A/B Testing • Experimentation • Causal Inference • Generative AI • Large Language Models • LLMs • Data Visualization • Power BI • Tableau • TensorFlow • PyTorch • Databricks • Spark • Cloud Computing • AWS • Azure • Google Cloud • MLOps • Git • Feature Engineering • Exploratory Data Analysis • Predictive Modeling

💡 Final Perspective

💡 Why This Opportunity Stands Out

The Associate Data Scientist position at Fortive combines foundational data science responsibilities with exposure to Generative AI, experimentation, enterprise analytics, cloud technologies, and MLOps. With a 0–2 year experience range and acceptance of academic, internship, personal, and open-source project experience, it is positioned as an entry-level opportunity for candidates building their professional data science careers.