AI Applied Data Scientist Jobs

Discover the latest remote and onsite AI Applied Data Scientist roles across top active AI companies. Updated hourly.

Check out 13 new AI Applied Data Scientist opportunities posted on The Homebase

Senior Data Scientist

New
Top rated
Faculty
Full-time
Full-time
Posted

Designing and building agents in high-consequence environments where outputs need to be validated to a high standard, performing exploratory data analysis, model building, validation, and performance monitoring, leading data science efforts within cross-functional delivery teams by partnering with engineers, designers, and product leads for successful outcomes, understanding deeply core customer problems to ensure technical solutions drive real value, and translating real-world problems into technical strategies and measuring model impact with scientific rigor.

Undisclosed

()

London, United Kingdom
Maybe global
Hybrid

Data Scientist

New
Top rated
Metropolis
Full-time
Full-time
Posted

Collect, process, and analyze large datasets from multiple sources Build and deploy machine learning models to solve business problems Design and implement A/B tests and statistical analyses Collaborate with cross-functional teams (product, engineering, marketing) to define analytics requirements Communicate complex data insights in a clear and actionable manner to stakeholders Develop dashboards and visualizations to monitor key metrics Stay current with the latest trends and technologies in data science and AI

Undisclosed

()

Bengaluru, India
Maybe global
Onsite

Senior Data Scientist, Marketing

New
Top rated
Harvey
Full-time
Full-time
Posted

The Senior Marketing Data Scientist will partner closely with Harvey’s Marketing organization to build the marketing data science function from the ground up. Responsibilities include embedding deeply with the Marketing organization as a trusted partner to identify opportunities to improve performance and drive growth, defining, tracking, and evolving core metrics across marketing and business functions, and building scalable dashboards and reporting frameworks that enable data-driven decision-making. The role involves designing, implementing, and evaluating models such as multi-touch attribution, marketing mix modeling, and incrementality for comprehensive Marketing Channel and Campaign performance and contribution. The Senior Data Scientist will apply statistical and machine learning techniques to model user behavior, forecast trends, and identify opportunities for growth and optimization. They will translate complex analyses into compelling stories with clear recommendations for cross-functional partners and executives, partner with Marketing, RevOps, and GTM Systems to co-develop data infrastructure ensuring robust pipelines, reliable data sources, and scalable systems to power analytics and modeling. The role also includes leading cross-functional analytics initiatives to synthesize competitive dynamics, customer feedback, and market trends into actionable business opportunities and championing a data-informed culture by establishing best practices, mentoring peers, and shaping the strategic role of data science at Harvey.

$170,000 – $200,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite

Data Scientist | ML

New
Top rated
Machinify
Full-time
Full-time
Posted

Master understanding of various claims payment policies and healthcare concepts; build and improve models by thoughtfully curating data and creating features; precisely measure and improve model performance against real-world outcomes and make operational recommendations to optimize model results; interpret and refine large-scale data created by complex business workflows; advance the team’s capabilities by improving pipelines, infrastructure, and tools.

Undisclosed

()

Palo Alto, United States
Maybe global
Remote

Signal Engineer

New
Top rated
Maincode
Full-time
Full-time
Posted

The Signal Engineer is responsible for designing, sourcing, shaping, and evaluating input signal at scale to enable AI models to learn faster, learn better, and generalize more effectively. This includes working deeply with raw and synthetic data, designing new datasets and signal mixtures, iterating on input distributions and formats, understanding how small changes in signal affect training behavior, thinking across stages of training rather than in isolation, and treating data as a dynamic, evolving system rather than a static asset. The role involves immersion, experimentation, and developing intuition through long feedback loops, using computational environments and technical tooling, working directly with data through scripts, notebooks, and experimental pipelines, modifying existing systems, and building small utilities to explore questions.

A$150,000 – A$180,000
Undisclosed
YEAR

(AUD)

Melbourne, Australia
Maybe global
Onsite

Principal Data Scientist

New
Top rated
PhysicsX
Full-time
Full-time
Posted

Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

Undisclosed

()

Shoreditch, Singapore
Maybe global
Hybrid

Quantum Computing Researcher

New
Top rated
Maincode
Full-time
Full-time
Posted

The role involves designing, sourcing, shaping, and evaluating input signal at scale to improve AI model learning speed, quality, and generalization across the entire training lifecycle. Responsibilities include working deeply with raw and synthetic data, designing new datasets and signal mixtures, iterating on input distributions and formats, understanding small changes in signal and their effects on training behavior, and considering the entire training stages as a dynamic and evolving system rather than as static assets. It also requires immersion, experimentation, and developing intuition through long feedback loops.

A$150,000 – A$180,000
Undisclosed
YEAR

(AUD)

Melbourne, Australia
Maybe global
Onsite

Senior Data Scientist

New
Top rated
LMArena
Full-time
Full-time
Posted

As a Senior Data Scientist at LMArena, you will explore and analyze large, complex datasets to uncover patterns, biases, and causal relationships in model behavior and system performance. You will formulate hypotheses about data quality, evaluation outcomes, and model performance, then design experiments to validate or refute them. You will build reproducible analysis pipelines using Python, Pandas, NumPy, and Spark to process and interrogate large-scale data. You will partner with ML researchers and engineers to design metrics and analyses that evaluate how models perform across domains, prompts, and tasks. Additionally, you will develop causal reasoning frameworks and statistical methods to explain model behavior beyond performance metrics. You will communicate insights clearly to both technical and non-technical partners, informing research direction and infrastructure improvements.

Undisclosed

()

Bay Area, United States
Maybe global
Remote

Senior Data Scientist

New
Top rated
Fyxer
Full-time
Full-time
Posted

The Senior Data Scientist will own Fyxer AI’s data science capabilities, setting the roadmap for key business areas like marketing and retention, implementing scalable solutions, and ensuring stakeholders use data to make confident commercial decisions. Responsibilities include building and refining predictive models on multi-channel customer and usage data to drive product and marketing decisions, collaborating with engineering, marketing, sales, and product teams to define KPIs, experiment with new algorithms, and surface actionable insights that drive impact. They will maintain data infrastructure including BigQuery, dbt, and Fivetran, ensure data quality for reporting and self-service analytics, and develop a culture of data-driven decision making by proactively suggesting improvements to tools, processes, and architecture.

£100,000 – £140,000
Undisclosed
YEAR

(GBP)

London, United Kingdom
Maybe global
Hybrid

Data Scientist - Manufacturing Data (KR)

New
Top rated
Gauss Labs
Full-time
Full-time
Posted

Design and implement customized AI solutions for manufacturing; analyze manufacturing data to uncover opportunities and develop AI models; collaborate with customers to understand their requirements and deliver clear, data-driven solutions; work closely with internal teams to ensure solutions are feasible, scalable, and aligned with product strategy; present findings to both technical and non-technical stakeholders.

Undisclosed

()

Yeoksam or Seoul, South Korea
Maybe global
Onsite

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Frequently Asked Questions

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[{"question":"What does a AI Applied Data Scientist do?","answer":"AI Applied Data Scientists develop statistical models and machine learning algorithms to solve business problems. They analyze complex datasets to extract insights, identify patterns, and drive decision-making. Their responsibilities include preprocessing data, designing experiments, conducting A/B tests, and measuring solution effectiveness. They collaborate with data engineers and stakeholders to build data pipelines, communicate findings through visualizations, and deploy scalable machine learning models while monitoring their performance."},{"question":"What skills are required for AI Applied Data Scientist?","answer":"The role requires proficiency in programming languages like Python, R, and SQL, plus experience with machine learning frameworks for building predictive models. Strong statistical analysis abilities are essential for feature selection and data interpretation. Familiarity with data visualization tools helps in creating effective dashboards. Experience with A/B testing, telemetry data analysis, and LLMs/prompt engineering is increasingly valuable. Collaboration skills are necessary for working across teams to implement solutions."},{"question":"What qualifications are needed for AI Applied Data Scientist role?","answer":"Employers typically seek candidates with at least 1-5 years of experience in applied data science or quantitative roles. A background in algorithms, A/B testing, and product analytics is important. Proficiency in SQL and Python for experiments and metrics tracking is essential. Experience with data pipelines, metrics creation, and trend analysis strengthens applications. Many positions prefer candidates with knowledge of NLP, large language models, or generative AI technologies."},{"question":"What is the salary range for AI Applied Data Scientist job?","answer":"The research provided doesn't include specific salary information for AI Applied Data Scientist positions. Compensation typically varies based on factors including geographic location, industry, company size, years of experience, and specific technical expertise. Salaries often reflect the specialized nature of combining AI knowledge with applied data science skills, which commands higher compensation than general data analysis roles in most markets."},{"question":"How long does it take to get hired as a AI Applied Data Scientist?","answer":"The hiring timeline for AI Applied Data Scientist positions isn't specified in the research. The process typically involves multiple interview rounds testing technical skills, problem-solving abilities, and domain knowledge. Candidates with experience in machine learning algorithms, statistical modeling, and programming languages like Python may progress more quickly. The hiring process can extend longer for roles requiring specialized AI knowledge or when companies conduct rigorous technical assessments."},{"question":"Are AI Applied Data Scientist job in demand?","answer":"While the research doesn't provide specific demand numbers, industry signals suggest AI Applied Data Scientist roles are growing in importance as businesses increasingly rely on predictive analytics and machine learning solutions. The specialized intersection of AI knowledge with applied data science skills makes these professionals valuable across industries. Companies seek candidates who can translate complex data into actionable business insights while building and implementing machine learning models."}]