We are seeking a highly experienced Data Science Lead Specialist Engineer with strong expertise in advanced analytics, machine learning, statistical modeling, and data engineering. The ideal candidate will be responsible for driving end-to-end data science initiatives, leading technical teams, collaborating with business stakeholders, and developing scalable AI and ML solutions that solve complex business problems.
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Lead data science strategy, architecture, design, and implementation across multiple business units.
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Work closely with stakeholders to identify business opportunities, gather requirements, and translate them into analytics and ML models.
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Develop predictive and prescriptive models leveraging machine learning, deep learning, NLP, and other advanced techniques.
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Perform large-scale data exploration, data mining, feature engineering, and model optimization.
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Design and implement scalable pipelines for model training, deployment, and monitoring in production environments.
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Lead a team of data scientists, analysts, and ML engineers, providing technical mentorship and guidance.
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Evaluate new technologies, tools, and frameworks to improve model performance and efficiency.
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Present actionable insights and deliver executive-level data-driven recommendations.
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Ensure data governance, quality assurance, security, and compliance best practices.
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Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field.
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12+ years of experience in Data Science, Machine Learning, AI engineering, or related areas.
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Strong programming skills in Python, R, Scala, and SQL.
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Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, Spark MLlib.
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Strong experience with cloud platforms such as AWS, Azure, or Google Cloud, including ML Ops capabilities.
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Proven experience designing and deploying production-grade machine learning solutions.
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Expertise in statistical modeling, predictive analytics, optimization, deep learning, and NLP techniques.
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Experience working with big data technologies such as Spark, Hadoop, Hive, Kafka, Databricks.
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Excellent communication skills with ability to clearly present complex analytics to non-technical stakeholders.
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Leadership experience managing teams and driving enterprise-wide initiatives.
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PhD in a quantitative discipline or equivalent advanced research background.
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Experience in data visualization tools such as Power BI, Tableau, or Looker.
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Experience with DevOps and ML Ops tools such as Docker, Kubernetes, Airflow, MLflow, Kubeflow.
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Experience in Generative AI, LLM fine-tuning, and vector databases.
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Experience working in regulated industries such as Finance, Healthcare, or Insurance.
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Ability to architect large-scale data science platforms and automation frameworks.