Manager - Lead Data Scientist

DXC Technology

10h

About The Position

Job Description Strategic & Leadership Responsibilities Lead end-to-end data science projects from problem definition to deployment and monitoring. Business Analysis: Translate business challenges into data science problems and propose optimal solutions. Mentor junior data scientists and analysts, fostering a culture of continuous learning and experimentation. Collaborate with stakeholders aligning data science initiatives with strategic goals. Provides advisory and consultation services to determine the right solution Participates in Technical/Work product Reviews Contributes to knowledge tools and communities, and ensures project learnings are documented and shared. Knowledge sharing and re-use within practice or profession. Mentors junior team members, providing technical assistance and constructive feedback. Technical Responsibilities Design, develop, and validate predictive models, classification algorithms, and other data science assets using statistical and machine learning techniques. Perform exploratory data analysis, feature engineering, and model tuning to improve performance. Build scalable data pipelines and integrate models into production environments in collaboration with data engineering and MLOps teams. Evaluate model performance using appropriate metrics and ensure robustness, fairness, and explainability. Stay current with advancements in AI/ML and recommend adoption of relevant technologies and methodologies. Operational Responsibilities Document methodologies, workflows, and model assumptions for transparency and reproducibility. Ensure compliance with data governance, privacy, and ethical AI standards. Collaborate closely with data engineers, analysts, and business stakeholders to define requirements and translate them into analytical solutions. Qualifications & Experience: Education and Experience: 8+ years of professional experience as a Data Scientist/ AI/ML Engineer and a Bachelor of Arts/Science or equivalent degree in computer science or related area of study; without a degree, three additional years of relevant professional experience (10+ years in total). Proven track record of delivering impactful data science solutions in enterprise environments. Experience working with large-scale structured and unstructured data. Technical Skills: Proficiency in Python, R, SQL, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). Strong understanding of statistical modeling, machine learning, deep learning, NLP, GenAI Strong understanding and execution of statistical concepts (correlation, confidence interval, time series forecasting, linear regression etc.) Experience with cloud platforms (Preferred: Azure, GCP, AWS ) specifically cloud-based AI/ML tools and containerization tools (Docker, Kubernetes). Experience with LLMs, RAG, LangChain, Generative AI, Pyspark Knowledge of data visualization tools (Power BI, Tableau, Plotly). Familiarity with MLOps practices and tools (MLflow, Airflow, CI/CD pipelines). Soft Skills: Strong problem-solving and critical thinking abilities. Ability to manage multiple projects and prioritize effectively. Collaborative mindset with a proactive approach to stakeholder engagement. Excellent communication and storytelling skills to convey complex insights to non-technical stakeholders. Strong time management practice, able to prioritize project demands within the allocated time. Champion of continuous learning

Requirements

8+ years of professional experience as a Data Scientist/ AI/ML Engineer and a Bachelor of Arts/Science or equivalent degree in computer science or related area of study; without a degree, three additional years of relevant professional experience (10+ years in total).
Proven track record of delivering impactful data science solutions in enterprise environments.
Experience working with large-scale structured and unstructured data.
Proficiency in Python, R, SQL, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
Strong understanding of statistical modeling, machine learning, deep learning, NLP, GenAI
Strong understanding and execution of statistical concepts (correlation, confidence interval, time series forecasting, linear regression etc.)
Experience with cloud platforms (Preferred: Azure, GCP, AWS ) specifically cloud-based AI/ML tools and containerization tools (Docker, Kubernetes).
Experience with LLMs, RAG, LangChain, Generative AI, Pyspark
Knowledge of data visualization tools (Power BI, Tableau, Plotly).
Familiarity with MLOps practices and tools (MLflow, Airflow, CI/CD pipelines).
Strong problem-solving and critical thinking abilities.
Ability to manage multiple projects and prioritize effectively.
Collaborative mindset with a proactive approach to stakeholder engagement.
Excellent communication and storytelling skills to convey complex insights to non-technical stakeholders.
Strong time management practice, able to prioritize project demands within the allocated time.
Champion of continuous learning

Nice To Haves

Microsoft Certified: Azure Data Scientist Associate or Azure AI Engineer Associate
Databricks Data Scientist professional
Google Professional Machine Learning Engineer
AWS Certified Machine Learning

Responsibilities

Lead end-to-end data science projects from problem definition to deployment and monitoring.
Translate business challenges into data science problems and propose optimal solutions.
Mentor junior data scientists and analysts, fostering a culture of continuous learning and experimentation.
Collaborate with stakeholders aligning data science initiatives with strategic goals.
Provides advisory and consultation services to determine the right solution
Participates in Technical/Work product Reviews
Contributes to knowledge tools and communities, and ensures project learnings are documented and shared.
Knowledge sharing and re-use within practice or profession.
Mentors junior team members, providing technical assistance and constructive feedback.
Design, develop, and validate predictive models, classification algorithms, and other data science assets using statistical and machine learning techniques.
Perform exploratory data analysis, feature engineering, and model tuning to improve performance.
Build scalable data pipelines and integrate models into production environments in collaboration with data engineering and MLOps teams.
Evaluate model performance using appropriate metrics and ensure robustness, fairness, and explainability.
Stay current with advancements in AI/ML and recommend adoption of relevant technologies and methodologies.
Document methodologies, workflows, and model assumptions for transparency and reproducibility.
Ensure compliance with data governance, privacy, and ethical AI standards.
Collaborate closely with data engineers, analysts, and business stakeholders to define requirements and translate them into analytical solutions.