PhD or Master’s degree in Computer Science, Statistics, or related field., 6+ years of applied research experience (or 4+ with PhD)., 3+ years of hands-on experience building, deploying, and monitoring production-grade ML models., Comprehensive understanding of deep learning concepts and proficiency in Python and PyTorch..
Key responsabilities:
Lead the development of deep learning-driven personalization algorithms for tailored user experiences.
Design and deploy predictive lead scoring models to optimize customer acquisition and retention strategies.
Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing and real-time inference.
Collaborate with cross-functional teams to turn novel research into scalable, production-grade systems.
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What do we do?
Intelliswift Software Inc. conceptualizes, builds, and supports the world's most amazing technology products and solutions. Our team of rich experts from diverse backgrounds contributes to making Intelliswift one of the most reliable partners in IT and Talent solutions. We specialize in delivering world-class Digital Product Engineering, Data Management and Analytics, and Staffing Solutions services to Fortune companies, SMBs, ISVs, and fast-growing startups.
To whom do we cater?
Industries: Technology, Media, Telecom, Pharma, Healthcare, Banking & Finance, Retail
Where are we headed?
We are driven to leverage our innovation and deep expertise to create a long-lasting impact for our customers and stakeholders.
What drives us?
• We believe in delivering sustainable and future-driven solutions.
• We believe in enabling everyone that works with us – from clients to partners to employees.
• We believe that the future belongs to those who Love the New – which is why we are constantly reinventing and innovating NEW solutions to enable businesses to stay ahead of the competition.
Pay rate range - $95/hr. to $98/hr. on W2
100% Remote
Must Have
Applied science
Deep Learning
Real-world experience in recommender systems, transformers, or multi-objective tasks.
REQUIRED SKILLS:
6+ years of applied research experience (or 4+ with PHD)
3+ years of hands-on experience building, deploying, and monitoring production-grade ML models
Job Description is tailor-made for the request
Years of Experience: 6+
Role Overview As an Applied Scientist specializing in personalization, lead scoring, and complex modeling, you will tackle cutting-edge challenges in machine learning and deep learning to redefine how our business engages with customers.
You will design and deploy high-impact models that drive customer segmentation, adaptive recommendations, and predictive lead prioritization.
Leveraging your expertise in deep learning, NLP, and general modeling, you’ll help build solutions that directly influence business outcomes, collaborating with cross-functional teams to turn novel research into scalable, production-grade systems.
Responsibilities * Lead the development of deep learning-driven personalization algorithms to deliver tailored user experiences across multiple channels (e.g., website, email, and others). * Design and deploy predictive lead scoring models to optimize customer acquisition, conversion, and retention strategies using advanced techniques like survival analysis, graph networks, or transformer-based architectures. * Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference. * Published research, filed patents, and stayed ahead of industry trends in the personalization and customer intelligence / lead scoring domains. * Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance personalization and lead scoring accuracy. * Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly. * Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker or MLflow and other internal tools. * Participate in science reviews to raise the science bar in our organization. This includes reviewing your work and the work of others.
Qualifications Basic Requirements * PhD or Master’s degree in Computer Science, Statistics, or related field * 6+ years of applied research experience (or 4+ with PHD) * 3+ years of hands-on experience building, deploying, and monitoring production-grade ML models * Comprehensive understanding of deep learning concepts * Proficiency in Python and PyTorch * Real-world experience in recommender systems, transformers, or multi-objective tasks. * Extensive knowledge in a breadth of machine learning topics * Strong background in statistical analysis, experimental design, and SQL/Spark for big data processing. * Ability to simplify complex concepts for stakeholders
Preferred Skills * Proven success in deploying deep learning models (e.g., BERT/Transformers for NLP, diffusion models, GANs or general DNNs) to solve business problems. * Experience working at other companies that operate at a similar large scale * Publications or patents in applied ML domains * Expertise in at least one focus area in each of the following: * MLOps: CI/CD pipelines, model monitoring, cloud platforms, Deployment strategy * Emerging Techniques: LLM fine-tuning, federated learning, automated feature engineering, siamese networks, backbones (feature extraction networks), and efficient transformer architectures. * Experience in at least one focus area in either of the following: * Personalization: Session-based and long-term interest recommendations. Two-Tower and Transformer-based architectures * Lead Scoring / Behavior: Predictive analytics, churn modeling, and causal ML for attribution.
Required profile
Experience
Industry :
Information Technology & Services
Spoken language(s):
English
Check out the description to know which languages are mandatory.