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Automl Jobs (NOW HIRING)

Data Scientist

Tampa, FL · On-site

$130K - $140K/yr

Drive continuous model improvement -- benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics * Partner with data and platform ...

Exposure in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology * Strong foundational skills in Linux Operating System. * Understanding of NLP, deep ...

Proficient in building functional AI models using techniques such as prompting and AutoML * Skilled in monitoring the performance of models in production and making necessary adjustments to ensure ...

Proficient in building functional AI models using techniques such as prompting and AutoML * Skilled in monitoring the performance of models in production and making necessary adjustments to ensure ...

Proficient in building or integrating functional AI models using techniques such as prompting and AutoML * Skilled in monitoring the performance of models in production and making necessary ...

Proficient in building or integrating functional AI models using techniques such as prompting and AutoML * Skilled in monitoring the performance of models in production and making necessary ...

... AutoML, and AI Infrastructure) and data engineering (e.g., S3, Data Factory, BigQuery). • 3+ years of experience in an AI/ML or data engineering role, with a strong portfolio of deployed AI/ML ...

Proficient in building or integrating functional AI models using techniques such as prompting and AutoML * Skilled in monitoring the performance of models in production and making necessary ...

Drive continuous model improvement - benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics * Partner with data and platform ...

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

Drive continuous model improvement -- benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics * Partner with data and platform ...

Use AI tools and platforms (like GPT, BigQuery ML, or AutoML) to generate and test hypotheses at scale. * Automate repetitive queries and reporting via scripts, bots, or AI copilots. * Communicate ...

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Automl information

What are some common challenges faced by professionals working in AutoML roles, and how can they be addressed?

Professionals in AutoML roles often encounter challenges related to automating complex machine learning workflows, ensuring model interpretability, and managing large-scale data pipelines. Balancing automation with customization to meet specific business needs can be tricky, as off-the-shelf solutions may not fit every scenario. Collaborating closely with data scientists, engineers, and domain experts helps in customizing AutoML solutions and overcoming integration issues. Staying updated on the latest tools and frameworks and continuously testing models in production are also essential for success.

What are the key skills and qualifications needed to thrive as an AutoML Engineer, and why are they important?

To thrive as an AutoML Engineer, you need strong proficiency in machine learning, data science, and programming (often Python), typically supported by a degree in computer science, data science, or a related field. Familiarity with AutoML platforms (such as Google AutoML, H2O.ai, or AutoKeras), cloud services, and experience with ML frameworks like TensorFlow or scikit-learn are essential. Analytical thinking, problem-solving abilities, and effective communication help you translate business needs into automated solutions and collaborate with cross-functional teams. These skills are vital for efficiently developing robust, scalable machine learning pipelines that accelerate model deployment and drive business value.

What is the difference between Automl vs Data Scientist?

AspectAutomlData Scientist
Required CredentialsTypically certifications in machine learning, data analysis, or related toolsDegree in data science, statistics, computer science, or related fields
Work EnvironmentFocus on developing and deploying automated machine learning models, often in tech or AI companiesAnalyze data, build models, and generate insights across various industries
Employer & Industry UsageUsed by companies seeking scalable ML solutions, including tech, finance, and healthcareEmployed across industries for data analysis, predictive modeling, and decision support

Automl focuses on automating machine learning processes, making it easier to develop models without extensive coding. Data Scientists, however, perform in-depth data analysis, model building, and interpretation. While Automl tools assist Data Scientists, their roles differ in scope and expertise required.

What is AutoML?

AutoML, or Automated Machine Learning, refers to the process of automating the end-to-end tasks of applying machine learning to real-world problems. This includes steps like data preprocessing, feature selection, algorithm selection, and hyperparameter tuning. AutoML tools are designed to make machine learning more accessible to non-experts and to improve efficiency for experts by reducing the manual effort and expertise needed to build effective models. Popular AutoML platforms include Google Cloud AutoML, H2O AutoML, and Auto-sklearn.
What are the most commonly searched types of Automl jobs? The most popular types of Automl jobs are:
Data Scientist

Data Scientist

Lorven technologies

Tampa, FL • On-site

$130K - $140K/yr

Full-time

Posted 19 days ago


Job description

Job Title: Data Scientist
Location: Austin, TX 78757 (or) Tampa, FL 33602 - Onsite 
Full-Time
 
As Data Scientist, you will spearhead the end-to-end development of sales forecasting and demand sensing models for CPG portfolios on Databricks (Azure). Work closely with commercial, supply chain, and engineering teams to build ML solutions that improve forecast accuracy, reduce inventory waste, and support revenue growth. Bring deep ML expertise, strong Python engineering skills, and a nuanced understanding of CPG market dynamics — and you are comfortable translating complex model outputs into clear business recommendations.
 
Skills / Experience:
  • 8+ years in data science or Applied ML roles; 3+ years of experience in Databricks in production
  • 5+ years of experience in Python — Pandas, PySpark, scikit-learn, Azure ML or Azure ecosystem and Databricks experience in production
  • 5+ years of experience in Supervised, unsupervised Machine Learning (ML) algorithms, forecasting and inventory optimization
  • 5+ years of experience in deep learning algorithms applying to solve forecasting, regression and classification problems
  • 3+ years of experience in building ML models in CPG, FMCG, or Retail analytics industry
  • 3+ years of experience in MLflow or equivalent experiment tracking tool 
  • Master's or PhD in Statistics, CS, or related field (preferred)
 
Job / Role Description:
  • Lead end-to-end sales forecasting model development — from data sourcing and feature engineering through model training, validation, and productionisation on Databricks (Azure)
  • Design and maintain forecasting pipelines — at SKU, category, and regional hierarchy levels — incorporating POS data, promotional calendars, seasonality indices, and external signals (macroeconomic, weather)
  • Apply CPG domain knowledge — to model promotional uplift, new product introduction curves, product cannibalization, and retailer sell-in/sell-out dynamics into ML features and targets
  • Operationalise ML models using MLflow on Databricks — manage the model registry, version control experiments, automate retraining schedules, and configure drift monitoring alerts
  • Collaborate with commercial and supply chain teams — to translate forecast outputs into inventory recommendations, production planning inputs, and revenue growth strategies
  • Define and enforce data science best practices — modelling standards, experiment documentation, code review guidelines, and reproducibility requirements across the team
  • Mentor junior data scientists — conduct code reviews, lead knowledge-sharing sessions, support career development, and build a high-performance data science culture
  • Communicate model insights and forecast accuracy — to senior stakeholders through dashboards, executive briefings, and written reports — making complex model behaviour accessible to business audiences
  • Drive continuous model improvement — benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics
  • Partner with data and platform engineers — to ensure feature pipelines on Azure Data Lake / Delta Lake are reliable, scalable, and aligned with model refresh cadence requirements
  • Communicate effectively with internal and customer stakeholders; Strong interpersonal skills to build and maintain productive relationships with team members
  • Problem-Solving and Analytical Thinking; Capability to troubleshoot and resolve issues efficiently
  • Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
  • Provides regular updates, proactive and due diligent to carry out responsibilities
Expected Outcome / What Success Looks Like
  • Data Scientist is expected to meet customer expectations within accelerated timelines, enabling us to strengthen our capabilities and drive growth in this area
  • Statistical Analysis & Experimentation - A/B testing, causal inference, and hypothesis testing to measure the business impact of model improvements and pricing interventions
  • This role offers the opportunity to lead high-impact data science initiatives that directly shape customer outcomes and gain strong visibility with senior leadership

Lorven technologies logo

About Lorven technologies

Sourced by ZipRecruiter

Lorven Technologies, headquartered in Plainsboro, New Jersey, United States, is a reputable company in the technology industry, specializing in providing effective IT solutions and consulting services. The company's official website, lorventech.com, offers comprehensive insights into its offerings which include but are not limited to software development, IT consulting, project management, and business analysis. Since its inception, Lorven Technologies has been committed to ensuring efficiency and reliability in delivering IT services to its global clientele, establishing itself as a trusted name in the industry.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Plainsboro, NJ, US

Year founded

2001

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