

Stott and May
Data Scientist
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with strong production experience, focusing on applied analytics and ML initiatives. Contract length is six months, with competitive hourly pay. Key skills include Python, SQL, and ML frameworks. Fully remote, aligned to EST.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
960
-
ποΈ - Date
February 18, 2026
π - Duration
More than 6 months
-
ποΈ - Location
Remote
-
π - Contract
W2 Contractor
-
π - Security
Unknown
-
π - Location detailed
New York, United States
-
π§ - Skills detailed
#Datasets #Model Deployment #ML (Machine Learning) #Monitoring #Agile #Azure #AWS (Amazon Web Services) #Data Engineering #Data Science #GCP (Google Cloud Platform) #PyTorch #SQL (Structured Query Language) #Statistics #Deployment #Cloud #Python
Role description
Weβre working with a digital client looking to bring on an experienced Data Scientist to support applied analytics and ML initiatives across an established cloud data platform.
Youβll join an existing delivery team as a senior individual contributor, working hands on across modeling, experimentation, and production deployment, with visibility across multiple business domains.
This suits someone practical and commercially minded, comfortable taking models from idea through to production.
Six month initial contract with strong likelihood of rolling extensions.
Competitive hourly rates depending on experience, 40 hrs/week.
Engagement via W2 or personal LLC only. No third parties.
Fully remote, aligned to EST.
What youβll be doing
β’ Building predictive and statistical models for real business use cases
β’ Working with large structured and semi structured datasets
β’ Partnering with data engineering to productionize models
β’ Designing experiments and validating model performance
β’ Translating business problems into analytical solutions
β’ Supporting model monitoring and continuous improvement
What weβre looking for
β’ Strong experience as a Data Scientist in production environments
β’ Solid Python and SQL
β’ Experience with ML frameworks (scikit-learn, XGBoost, PyTorch, etc.)
β’ Strong statistics and modeling fundamentals
β’ Comfortable working with messy, real-world data
β’ Experience in agile, delivery-focused teams
Nice to have
β’ Cloud experience (AWS / Azure / GCP)
β’ Feature engineering and model deployment exposure
β’ Experience supporting product or growth teams
β’ Basic MLOps familiarity
Why this role
β’ Applied data science, not research
β’ Real business impact
β’ Flexible engagement model, extensions likely
β’ Opportunity to work with experienced data teams
Weβre working with a digital client looking to bring on an experienced Data Scientist to support applied analytics and ML initiatives across an established cloud data platform.
Youβll join an existing delivery team as a senior individual contributor, working hands on across modeling, experimentation, and production deployment, with visibility across multiple business domains.
This suits someone practical and commercially minded, comfortable taking models from idea through to production.
Six month initial contract with strong likelihood of rolling extensions.
Competitive hourly rates depending on experience, 40 hrs/week.
Engagement via W2 or personal LLC only. No third parties.
Fully remote, aligned to EST.
What youβll be doing
β’ Building predictive and statistical models for real business use cases
β’ Working with large structured and semi structured datasets
β’ Partnering with data engineering to productionize models
β’ Designing experiments and validating model performance
β’ Translating business problems into analytical solutions
β’ Supporting model monitoring and continuous improvement
What weβre looking for
β’ Strong experience as a Data Scientist in production environments
β’ Solid Python and SQL
β’ Experience with ML frameworks (scikit-learn, XGBoost, PyTorch, etc.)
β’ Strong statistics and modeling fundamentals
β’ Comfortable working with messy, real-world data
β’ Experience in agile, delivery-focused teams
Nice to have
β’ Cloud experience (AWS / Azure / GCP)
β’ Feature engineering and model deployment exposure
β’ Experience supporting product or growth teams
β’ Basic MLOps familiarity
Why this role
β’ Applied data science, not research
β’ Real business impact
β’ Flexible engagement model, extensions likely
β’ Opportunity to work with experienced data teams






