Staff Data Scientist Machine Learning/Artificial Intelligence

Job description

<p><p><b>About the role :</b></p><p><br/></p><p>We are looking for a highly skilled Senior Data Scientist who can translate complex business problems into advanced analytical solutions.</p><br/><p>The ideal candidate combines deep expertise in statistical modeling, machine learning, and data engineering with a strong focus on Generative AI (GenAI), Large Language Models (LLMs), and Deep Learning.</p><br/><p>This role requires both technical leadership and strategic visionfrom driving AI/ML innovation to mentoring junior team members, and collaborating across product, consulting, and engineering teams to bring AI-powered solutions to life.</p><br/><p>By applying your expertise in data science to the ad-server and retail media domains, you will contribute to delivering targeted and impactful advertising campaigns, optimizing ad inventory, and maximizing ROI .</p><br/><p><b>What will you do?</b></p><br/><p>Your role in building ML algorithms for the ad server and retail media would involve the following : </p><br/><p><b>Business Problem Solving : </b></p><p><br/></p><p>- While technical proficiency in data manipulation, statistical modelling, and machine learning is crucial, the ability to apply these skills to solve real-world business problems is equally vital.</p><br/><p>- Translate complex business challenges into analytical frameworks using statistical and ML methodologies.</p><br/><p>- Apply AI/ML solutions to real-world business use cases across multiple industries.</p><br/><p>- Extract insights from large-scale datasets to improve model accuracy, fairness, and relevance.</p><br/><p><b>Technical Proficiencies :</b></p><p><br/></p><p>- Strong expertise in statistical modelling, machine learning, deep learning, and NLP, with hands-on experience in LLMs and Generative AI (fine-tuning, RAG, prompt engineering).</p><br/><p>- Proficiency in Python, PyTorch/TensorFlow, Hugging Face, and cloud-based ML platforms (AWS/GCP/Azure) with MLOps practices.</p><br/><p>- Skilled in building scalable data pipelines and ML systems for training, evaluation, and deployment at scale.</p><br/><p>- Solid grounding in Responsible AI principles including fairness, explainability, and bias mitigation.</p><br/><p><b>AI/ML & Generative AI Innovation : </b></p><p><br/></p><p>- Lead the design, development, and deployment of scalable ML and DL solutions, with emphasis on LLMs and GenAI.</p><br/><p>- Build training, fine-tuning, evaluation, and serving pipelines for LLMs across use cases like content generation, summarization, semantic search, personalization, and multimodal AI.</p><br/><p>- Drive applied research, incorporating the latest in GenAI and foundation models into production-ready systems.</p><br/><p>- Ensure solutions are optimized for performance, security, reliability, and cost.</p><br/><p><b>Leadership & Collaboration :</b></p><p><br/></p><p>- Provide technical and strategic leadership to cross-functional teams of Data Scientists and Analysts.</p><br/><p>- Collaborate with engineering and product teams to integrate AI solutions into customer-facing platforms.</p><br/><p>- Mentor junior data scientists; set and promote best practices in MLOps, Responsible AI, and ML system design.</p><br/><p>- Stay ahead of industry trends and incorporate them into roadmap planning.</p><br/><p><b>Project Management & Strategy :</b></p><p><br/></p><p>- Drive the execution of business plans and projects.</p><br/><p>- Manage the continuous improvement of data science initiatives.</p><br/><p>- Direct the gathering and assessment of data for project goals.</p><br/><p>- Develop contingency plans and adapt to changing business needs.</p><br/><p><b>You will be a great fit if you have : </b></p><br/><p>- A master's or doctoral degree in a relevant field such as computer science, statistics, mathematics, or data science is preferred.</p><br/><p>- A strong academic background with coursework in machine learning, statistical modeling, data mining, and programming is valuable.</p><br/><p>- 8 -12 years of practical experience in data science, generative AI, machine learning, and analytics.</p><br/><p>- Experience in relevant domains, such as e-commerce, advertising, or retail, may be advantageous.</p><br/><p>- Minimum 34 years of deep, hands-on expertise in NLP, LLMs, and Generative AI.</p><br/><p>- 2+ years of Demonstrated experience in leading projects and teams.</p><br/><p>- Advanced Proficiency in Python is essential for data manipulation, statistical analysis, and machine learning model development.</p><br/><p><b>Core Skills : </b></p><br/><p>- Strong foundation in machine learning, deep learning, and NLP.</p><br/><p>- Hands-on experience with LLMs (GPT, LLaMA, Falcon, Mistral, etc.) and GenAI frameworks (LangChain, Hugging Face, RAG, fine-tuning, LoRA).</p><br/><p>- Proficiency in Python, PyTorch/TensorFlow, SQL, and cloud platforms (GCP/AWS/Azure).</p><br/><p>- Experience with MLOps frameworks (MLflow, Kubeflow, Vertex AI, SageMaker, etc.</p><br/><p>- Strong data engineering skills to build ETL pipelines for large-scale datasets.</p><br/><p>- Statistical rigor : hypothesis testing, power analysis, confidence intervals; regression (GLM/GLMM), time-series; causal inference (propensity scores, DiD, RDD) for business impact.</p><br/><p><b>Soft Skills : </b></p><br/><p>- Strong business acumen and ability to map AI/ML solutions to strategic goals.</p><br/><p>- Excellent communication and stakeholder management skills.</p><br/><p>- Proven track record of mentoring and leading teams, collaborating with cross-functional teams, and communicating effectively with stakeholders.</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Mathematical Science Occupations