Forward Deployed AI Strategist

Data Analyst

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

Senior AI Engineer

Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Proactive Engineer

Role Description: <ul><li>The Strategy team is looking for a hands-on AI-enabled strategy and market intelligence professional to materially accelerate the way we conduct deep research, market intelligence, analytical modeling, and GTM analysis by applying high impact AI workflows and translating them into sharper strategic decisions.</li><li>The individual will partner with business, product, commercial, and tech teams to translate ambiguous questions into actionable insights, while developing and scaling AI-enabled solutions that improve strategic decision-making and drive measurable business impact.&nbsp;</li><li>The individual is expected to work in close partnership with our client IT team, to leverage existing AI &amp; data capabilities on Databricks &amp; AWS (where applicable) while developing solutions.</li></ul> Responsibilities: Strategic problem solving and driving cross-functional execution&nbsp; <ul><li>Translate AI-assisted analyses into practical executive decisions by applying strong business judgment, rigorous validation protocols, and a sharp “so-what” orientation</li><li>Collaborate with and influence senior stakeholders across strategy, sales, technology teams, and P&amp;L owners</li><li>Good first-principles and problem-solving capabilities with the ability to build and test strategic hypotheses Identifying meaningful opportunities to leverage AI and deploying scaled AI solutions at pace</li></ul> Identify, assess, and prioritize high-value AI and automation opportunities by translating business challenges into scalable, practical solutions&nbsp; <ul><li>Design proprietary AI-enabled workflows, agents, and accelerators for recurring strategy activities (competitor monitoring, earnings call tracking, tariff and regulatory intelligence, voice-of-customer mining etc.)</li><li>Build reusable templates, workflows, and modules to drive adoption and embed AI capabilities into strategy processes</li></ul> Executing rapid AI-augmented research and competitive intelligence&nbsp; <ul><li>Lead AI-augmented research using tools like Claude, OpenAI, Perplexity, AlphaSense, CapIQ etc. to deliver source-triangulated competitive and industry insights</li><li>Synthesize structured datasets (S&amp;P Capital IQ, Bloomberg, IDC, Gartner) and unstructured sources (10-Ks, analyst reports, expert interviews) – to generate and validate AI-assisted outputs</li></ul> Support strategy team in day-to-day execution by leveraging AI-led tools <ul><li>Lead AI-enabled market sizing, opportunity mapping, and TAM/SAM/PAM modelling</li><li>Accelerate financial modelling – scenario and sensitivity analyses, valuation, NPV/IRR, segment P&amp;Ls, pricing</li><li>Support M&amp;A pipeline by AI-screening targets and generating investment theses</li></ul> AI enablement, coaching and continuous innovation <ul><li>Coach strategy team members on prompting techniques, research workflows, and AI-fluent execution</li><li>Scale AI adoption across Corporate Strategy through hands-on mentorship of beginner users</li><li>Stay current on agentic stacks, multimodal models, and deep research agents</li></ul> Requirements: Skillset and capabilities AI fluency and hands-on deployment orientation&nbsp; <ul><li>Working knowledge of GenAI patterns – LLM capabilities/limitations, prompt design, etc.</li><li>Proven ability to convert business problems into reusable AI workflows, tools, or accelerators</li><li>Builder mindset with experience prototyping AI solutions using frontier tools &amp; low-code platforms</li></ul> Analytics, modelling, and research excellence <ul><li>Strong modelling skills across TAM/SAM/PAM, scenarios, sensitivities, NPV/IRR, valuation etc.</li><li>Ability to synthesize structured datasets with unstructured sources</li><li>Research discipline – source triangulation and rigorous validation of AI outputs</li></ul> Strategic problem solving, communication, executive presence, and influence&nbsp; <ul><li>Sharp "so-what" orientation to translate AI outputs into practical executive decisions</li><li>Crisp written and verbal communication, with ability to frame board-ready narratives</li><li>Skill in explaining AI workflows and implications to both technical and non-technical audiences</li><li>Stakeholder management across strategy, commercial, data, and technology teams</li></ul> Ownership, and execution <ul><li>Independently own workstreams end-to-end – from framing through deployment</li><li>High bar for quality; never publishes unchecked AI outputs or unvalidated analysis</li></ul> Coaching and capability building&nbsp; <ul><li>Patience and ability to coach colleagues on prompting, research workflows, and source validation</li><li>Track record of building reusable playbooks – templates and training modules that scale adoption</li></ul> Passion, curiosity, and learning agility <ul><li>Deep passion for AI, tech, and industrial innovation, with curiosity to stay current on frontier tools</li><li>Entrepreneurial ownership mindset and drive to build a new AI-enabled strategy capability</li></ul> Qualifications:&nbsp; <ul><li>BE / B-Tech in Computer Science Engineering, Data Science or a related analytical field required; MBA, MS, M-Tech or equivalent advanced degree strongly preferred.</li><li>3–4+ years of experience in AI transformation, analytics, or AI-enabled business building. Prior Forward Deployed Engineer / Solutions Architect / Senior Data Scientist background preferred.</li><li>Experience in a top-tier strategy consulting firm, AI-native consulting team, analytics firm or corporate / product strategy team role strongly preferred.</li><li>Demonstrated experience building or deploying 0–1 AI solutions (not just experimenting), GenAI, analytics, automation, or workflow solutions in a business environment required.</li><li>Proficiency in Excel and PowerPoint required; Python or equivalent scripting capability strongly preferred. Experience with DevOps, CI/CD pipelines, and infra as code (e.g., Jenkins, Docker, Kubernetes) required.</li><li>Proficient in implementing AI platforms like AWS Bedrock, LangGraph, Crew AI, Glean etc.</li><li>Knowledge of enterprise data environments (esp. SAP, Databricks) or cloud platforms (esp. AWS) is a plus. Experience integrating AI systems with enterprise data platforms required.</li><li>Experience with LLM-powered solutions, RAG systems, agentic workflows, AI research tools, or AI-enabled knowledge manage</li></ul>

Responsibilities: Database Maintenance &amp; Support <ul><li>Assist in the maintenance of relational databases (Oracle, SQL Server).</li><li>Support schema creation, updates, normalization, and performance tuning.</li><li>Monitor data quality, identify inconsistencies, and help ensure data integrity across systems.</li></ul> Data Migration &amp; Platform Transition <ul><li>Support data extraction, transformation, and loading (ETL) during system migrations and platform upgrades.</li><li>Assist in transitioning data between legacy environments and modern systems, including Databricks.</li><li>Validate migrated data and document reconciliation results. Data Analysis &amp; Querying</li><li>Write and execute SQL queries for analysis, verification, and operational needs in Oracle and SQL Server environments.</li><li>Participate in exploratory and diagnostic analysis to uncover trends and support business decisions.</li></ul> Documentation &amp; Governance <ul><li>Maintain accurate documentation of data processes, transformations, and system behaviors.</li><li>Support compliance with organizational standards for data governance and metadata management.</li></ul> Requirements: Required Skills &amp; Qualifications <ul><li>Bachelor’s degree in Computer Science, Data Analytics, Information Systems, Engineering, or related field.</li><li>Foundational understanding of relational database concepts and SQL.</li><li>Introductory experience or coursework using Oracle, SQL Server, or similar RDBMS platforms.</li><li>Strong analytical thinking, attention to detail, and problem solving skills.</li><li>Ability to collaborate with technical teams in a fast paced environment.</li></ul> Preferred Skills &amp; Qualifications <ul><li>Exposure to Databricks or other big data / cloud analytics technologies.</li><li>Understanding of ETL concepts, data pipelines, or data transformation frameworks.</li><li>Familiarity with PL/SQL, SSIS, or other database programming tools.</li><li>Interest in learning modern data lake, delta, or virtualization technologies (e.g., Denodo).</li></ul>

Responsibilities: <ul><li>Lead the design, development, and deployment of machine learning models using Python for applications including process optimization, predictive analytics, demand forecasting, computer vision, and decision support systems</li><li>Network across manufacturing, R&amp;D, commercial, and business functions to identify and collaboratively execute on high-impact challenges solvable through machine learning techniques</li><li>Build production-ready ML solutions within enterprise platforms (Databricks, Azure ML) including model training, endpoint deployment, and monitoring</li><li>Develop and maintain scalable, modular, well-documented code following MLOps best practices including version control, logging/metadata standards, and CI/CD principles</li><li>Lead code reviews, architectural discussions, and knowledge sharing to maintain high quality, production-ready deliverables across the team</li><li>Collaborate with domain experts and stakeholders across a wide variety of domains to understand needs, integrate ML solutions into workflows, and deliver measurable business value</li><li>Communicate complex machine learning methodology, results, and business impact clearly to technical and non-technical audiences through presentations, documentation, and visualizations</li><li>Stay current with advancements in machine learning, and apply them to solve business challenges</li><li>Mentor and provide technical guidance to data scientists, helping to grow the teams capabilities in Hyderabad</li><li>Support ML governance processes including architecture and methodology reviews, risk assessments, and compliance with enterprise standards</li><li>Lead project planning and execution, managing timelines, resources, and stakeholder expectations</li></ul> Requirements: <ul><li>Masters or Ph.D. in a relevant field such as Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a domain-focused field (Engineering, Chemistry, Economics, Finance, etc.)</li><li>5-8+ years of experience in data science, machine learning, or a related field with demonstrated impact</li><li>Strong foundation in statistical and machine learning theory with proven ability to select, apply, and evaluate appropriate techniques for complex, real-world problems</li><li>Extensive hands-on experience building predictive models and deploying them to production environments using Python</li><li>Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost, TensorFlow/PyTorch) and data manipulation libraries (pandas, polars, numpy)</li><li>Experience with solution architecting and design for ML systems</li><li>Hands-on experience with cloud platforms such as Azure and/or Databricks for deploying and managing ML models and solutions</li><li>Experience with version control (Git), collaborative development, and code review processes</li><li>&nbsp;Strong written and oral communication skills with ability to present technical concepts and results to diverse audiences from technical teams to business leadership</li><li>History of project leadership and driving initiatives from conception to production</li><li>Strong problem-solving skills and the ability to work independently as well as lead and mentor team members&nbsp;</li></ul>

Responsibilities: <ul><li>Support the AI use-case intake process, gather requirements, and rapidly assess strategic fit, value, complexity, and feasibility.</li><li>Maintain an AI use-case/solution inventory; identify duplication and promote reuse of existing platforms and components.</li><li>Evaluate alignment to the company’s AI strategy, enterprise architecture, data strategy, and target operating model.</li><li>Help establish guidelines for build/buy/hybrid and create recommendation artifacts for leadership and governance bodies.</li><li>Assess candidate architectures across cloud services, data platforms, model choices (LLMs, classical ML, CV, NLP), and integration patterns.</li><li>Compare third-party SaaS/managed services with in-house custom builds and hybrids; estimate TCO, scalability, maintainability, and time-to-value.</li><li>Support vendor evaluations and small proof-of-concept efforts, reviewing items like security practices, data handling, SLAs, integration needs, and roadmap alignment.</li><li>Partner with Procurement/Vendor Management on contracting, licensing, data processing, and exit strategies.</li><li>Partner with Engineering, Data Science, and DevOps/MLOps to scope pilots and define success metrics and guardrails.</li><li>Ensure handoffs include documentation, controls, and operational readiness</li><li>Develop playbooks, patterns, and templates to support AI solution reviews, and help enable business and tech teams through training sessions and office hours..</li><li>Track portfolio outcomes (benefits, risk findings, reuse rate) and continuously improve intake and evaluation processes.</li></ul> Requirements: <ul><li>Software engineering and cloud architecture knowledge across AWS/Azure; APIs; event/microservices; observability.</li><li>Good understanding of AI/ML and LLM stacks (vector databases, embeddings, RAG, prompt engineering, guardrails).</li><li>Understanding of privacy/security controls in financial services (data classification, encryption, DLP, IAM, segmentation) and third-party risk.</li><li>Proven ability to translate business objectives into technical evaluation criteria and clear decisions.</li><li>Excellent written and verbal communication for executive-level summaries and deep-dive technical reviews.</li><li>Critical thinker with structured problem-solving and sound judgment</li></ul>

Role Description: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Transition legacy data systems to Databricks.</li><li>Build and maintain robust, scalable data pipelines.</li><li>Work closely with subject matter experts, product teams, and stakeholders to understand business logic.</li><li>Apply best practices in data engineering and programming.</li><li>Mentor junior engineers and contribute to knowledge sharing.</li><li>Participate in code reviews and technical discussions.</li></ul> Requirements: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul> Why Join Us <ul><li>Opportunity to work on a strategic, high-impact data platform transformation.</li><li>Exposure to cutting-edge technologies including Databricks and cloud platforms.</li><li>Collaborative, inclusive team culture with strong mentorship.</li><li>Clear opportunities for career growth and skill development.</li></ul>

Role Overview: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.&nbsp;</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Lead the transition of legacy data systems to Databricks, ensuring reliability and performance.</li><li>Design, build, and maintain robust, scalable data pipelines to support analytical and operational use cases.</li><li>Collaborate closely with subject matter experts, product teams, and business stakeholders to understand and implement business logic accurately.</li><li>Apply data engineering and programming best practices to ensure high-quality, maintainable, and secure solutions.</li><li>Mentor junior engineers, fostering technical growth and promoting knowledge sharing across the team.</li><li>Participate in code reviews, architectural discussions, and technical decision-making to uphold engineering standards.</li></ul> Requirements:&nbsp; Required Skills &amp; Experience: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience: <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul>

Responsibilities: Architecture &amp; Design:&nbsp; <ul><li>Design end-to-end data architectures that integrate real-time streaming (Kafka, GCP Pub/sub) and batch processing (Big Query OR Databricks).</li></ul> Pipeline Development:&nbsp; <ul><li>Build and maintain robust ETL/ELT pipelines flowing into BigQuery/Databricks, ensuring data is clean, modeled, and ready for consumption.</li></ul> Custom Integration:&nbsp; <ul><li>Develop custom APIs and microservices using Java/Python/C# to facilitate data movement between niche systems and the data lakehouse.</li></ul> Infrastructure Management:&nbsp; <ul><li>Deploy and manage data workloads on Google Kubernetes Engine (GKE), ensuring high availability and cost-efficiency.</li></ul> Technical Leadership:&nbsp; <ul><li>Mentor junior engineers, conduct code reviews, and establish DataOps best practices (CI/CD, automated testing, and monitoring).</li></ul> Data Reliability:&nbsp; <ul><li>Implement data quality frameworks to ensure accuracy and lineage across the hybrid environment.</li></ul> Requirements: <ul><li>Experience with Terraform &amp; Azure Dev Ops (ADO).</li><li>Knowledge of workflow orchestrators like Apache Airflow (Cloud Composer).</li><li>Experience implementing Data Governance (metadata management, data cataloging).</li><li>Data Processing &amp; Storage</li><li>BigQuery: Expert-level SQL and experience with partition/cluster optimization.</li><li>Databricks: Deep knowledge of the Lakehouse architecture, Delta Lake, and Spark (PySpark/Scala) Software Engineering &amp; APIs</li><li>Languages: Proficiency in Java/Python/C# for backend development and custom data connectors.</li><li>API Design: Proven ability to build and consume RESTful APIs for data ingestion Streaming &amp; Infrastructure</li><li>Kafka/GCP Pub-Sub: Experience building event-driven architectures and managing producers/consumers.</li><li>GKE: Proficiency in containerization (Docker) and orchestrating data workloads on Kubernetes.</li></ul>

Responsibilities: <ul><li>Analyze DEX tool data to identify trends, predict issues, and propose proactive interventions to enhance employee digital experience.</li><li>Develop, test, and maintain scripts and tools using PowerShell and Python to automate repetitive tasks and improve system performance.</li><li>Work with data warehouses like Redshift and analytics platforms such as Databricks. Use SQL or another programming language to work with databases and create dashboards to extract insights and develop actionable recommendations.</li><li>Develop dashboards and reporting tools using SysTrack and Microsoft Power BI to provide visibility into key performance metrics.</li><li>Collaborate with IT Service Desk and Desktop Support Teams to identify opportunities for automation and efficiency improvements.</li><li>Proactively engage with stakeholders to understand business requirements and translate them into technical solutions.</li><li>Design and implement innovative ideas to enhance customer service and reduce the mean time to resolution (MTTR).</li><li>Act as a consultant to various teams, providing technical expertise in the use of DEX tools and scripting languages.</li><li>Lead initiatives to integrate new technologies or methodologies that improve the IT environment and employee satisfaction.</li><li>Conduct Root Cause Analysis (RCA) on recurring issues and propose permanent solutions.</li><li>Engage with vendor support resources to resolve complex technical challenges and evaluate new technologies.</li><li>Work with APIs to integrate and enhance system capabilities, ensuring seamless interaction between tools and platforms.</li></ul> Requirements: <ul><li>Proficiency in DEX tools like SysTrack, Nexthink, or similar platforms.</li><li>Strong coding skills in PowerShell and Python.</li><li>Hands-on experience with data analytics platforms, such as Databricks, and data warehouses like Redshift. Proficiency in SQL as a programming language for working with databases and creating dashboards.</li><li>Expertise in Microsoft Power Apps and Power BI for data visualization and automation.</li><li>Experience in working with APIs for system integration and enhancements.</li><li>Deep understanding of IT environments, including endpoint devices, software, and cloud-based tools.</li><li>Ability to interpret data and provide actionable insights to improve digital employee experience.</li><li>Strong problem-solving skills, with experience conducting RCA and developing innovative solutions.</li><li>Demonstrated ability to identify and act on trends and opportunities in emerging technologies.</li><li>Knowledge of IT Service Management frameworks (e.g., ITIL) and their practical application.</li></ul>

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8 databricks Jobs found

Forward Deployed AI Strategist

Data Analyst

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

Senior AI Engineer

Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Proactive Engineer

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8 databricks Jobs found

Forward Deployed AI Strategist

Data Analyst

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

Senior AI Engineer

Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Proactive Engineer

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