Commercial Planning (営業　企画系) の求人一覧 - コカ･コーラボトラーズジャパン株式会社

Role Purpose:The Lead ML Data Engineer is a senior technical leader responsible for enabling scalable, production-grade Data Science & Analytics (DSA) solutions within Coca-Cola Bottlers Japan Inc.'s (CCBJI) Vending Machines (VM) business unit. This role leads the development, optimization, and management of end-to-end ML and Analytics data workflows, ensuring reliable and efficient infrastructure for AI solutions like Assortment, Column Reallocation, and Placement.The role works in close partnership with Data Scientists, Analytics Specialists, and IT to deliver high-impact, ML-ready datasets via best of breed tools. The role plays a key function in bridging business objectives with technical delivery by converting commercial data requirements into robust pipelines, reusable assets, and operational tooling.The position also drives quality standards and data engineering practices within the team, ensuring model reproducibility, pipeline traceability, and integration with enterprise MLOps and governance frameworks.Key Responsibilities:Advanced ML Data Pipeline DevelopmentDesign, develop, and maintain robust data pipelines to support ML model training, inference, and feature transformation workflows.Deliver performant and modular pipelines using Databricks (PySpark/Python) and Snowflake, aligned to architectural best practices.Ensure end-to-end ownership of data engineering from working with IT on raw data ingestion to developing model-ready feature layers.Implement CI/CD-ready transformation logic for reproducibility and handover to downstream components.Feature Stores & Reusability FrameworkArchitect and maintain a centralized, scalable feature stores that enables reuse across multiple DS use cases.Define feature documentation standards, naming conventions, and lifecycle management practices.Optimize joins, aggregations, and lookups to balance compute cost with accuracy and inference performance.MLOps Integration & Model Lifecycle EngineeringWork closely with the Data Scientists and Analytics Specialists to operationalize models through CI/CD pipelines (MLflow, GitHub Actions, Databricks Workflows).Design robust systems for retraining triggers, monitoring, and automated evaluation of production ML models.Implement failure recovery, alerting, and model rollback procedures in collaboration with IT and DevOps.Engineering Excellence & Domain LeadershipServe as the go-to engineering authority for ML enablement within the DSA team.Lead technical design reviews, set code standards, and promote reusability and modularization across pipelines.Contribute internal tools, libraries, and utilities that improve engineering velocity and onboarding.Conduct informal mentoring and coaching for junior engineers and scientists working with data pipelines.Agile Program DeliveryActively participate in agile sprint cycles, contributing to planning, estimation, retrospectives, and delivery metrics.Align with the Analytics Portfolio Manager, DS Manager, and Analytics Manager to prioritize deliverables and resolve cross-functional dependencies.Maintain and manage engineering backlog, surfacing technical debt or architectural decisions that require executive alignment.Cross-Functional CollaborationTranslate ambiguous business requirements into structured, scalable data solutions that accelerate DS and Analytics outcomes.Collaborate with the Analytics team to build curated views and pre-aggregated layers for Power BI or experimentation workflows.Partner with IT to ensure infrastructure provisioning, access control, and platform governance align with CCBJI’s enterprise standards.Data Observability & Production AssuranceBuild and maintain monitoring systems to track pipeline performance, schema changes, and data freshness.Implement quality checks and exception handling to reduce operational risk and manual rework.Ensure SLAs are defined and met for model refreshes, data availability, and system uptime.Key Outputs:Stable, scalable ML data pipelines that serve predictive models across VM business scenarios.Well-documented and reusable feature store logic shared across DS initiatives.Fully operationalized MLOps workflows supporting model retraining and deployment.Toolkits, templates, standards and frameworks adopted by team members for faster pipeline delivery.Reduction in time-to-ML model deployment time and increased delivery velocity.Measurable improvements in pipeline stability, data quality, and platform observability with operational dashboardsPerformance Success Criteria (Examples):Launch production-grade ML pipelines for at least three strategic models within first 9–12 months.Reduce end-to-end model deployment cycle time by 25% through reusability and automation.Deliver a reusable feature store structure adopted by at least 3 DS initiatives.Implement and operationalize monitoring workflows covering pipeline reliability and data quality.Create internal engineering utilities or templates reused by at least two other team members.Maintain 98%+ reliability of ML workflows and data pipelines with documented support procedures.

ｶｽﾀﾏｰﾌﾟﾗﾝﾆﾝｸﾞ&ｶﾃｺﾞﾘｰﾏﾈｼﾞﾒﾝﾄ（東日本）担当課長ファンクション： National Sales Division雇用形態： 正社員正社員：   管理職募集人数：  1名職務概要・主な役割と責任【職務概要】チェーンストア向け営業と連携し、POSデータや市場分析をもとに売上最大化に向けた戦略提案を行うポジションです。 商品の提案にとどまらず、売場やカテゴリ全体の成長を見据えた提案を行い、顧客のビジネスに直接的に貢献できる点が魅力です。 戦略立案から実行まで一貫して携わることができるため、自身の施策が成果として可視化されやすく、大きなやりがいを感じられます。 また、複数部門と連携したプロジェクトにも関与でき、将来的なマネジメントや戦略人材としての成長機会も豊富です。【主な役割】1、チェーンストア向けに、売上拡大に向けたカテゴリー戦略の立案・実行、および各種プロジェクトの推進 2、お客様（カスタマー）および社内外の関係者と連携し、チャネル全体での売上成長をリード 3、担当エリアに留まらず他エリアも巻き込みながら、影響力を発揮したプロジェクト推進 4、顧客に対する売上拡大支援（カテゴリー戦略の策定・推進） 　・販売データや市場動向の分析に基づく課題抽出と改善提案 　・価格戦略や売上最大化施策の企画・推進 　・定期商談（月次など）における提案リード 　・売場づくり（棚割・スペース設計）の企画提案 　・売上拡大に向けた各種施策の実行・獲得 　・会議体への参加および関係者との合意形成 　・営業チームと連携したPDCA推進 5、メンバー育成・教育のサポート 6、営業本部の各種プロジェクトへの参画を通じた組織変革の推進と、多様な人材が活躍できる環境づくりへの貢献【働き方】・残業時間は月平均15時間程度と、ワークライフバランスを保ちながら働ける環境です。 ・在宅勤務（週2～3日目安）やフレックスタイム制度を活用し、自身のライフスタイルに合わせた柔軟な働き方が可能です。 ・女性社員も多く活躍しており、部内の約半数が女性と、性別に関わらず活躍できる環境です。 ・主体的なチャレンジを歓迎する風土があり、部門を越えて周囲を巻き込みながらプロジェクトを推進できる環境です。新しい取り組みを最後までやり切ることが評価されます。＜働き方のイメージ＞勤務地　 ：埼玉オフィス（さいたま市） 勤務内容：在宅勤務（週1-2回は埼玉オフィスへ出社） 担当エリア：埼玉、千葉、茨城、栃木、群馬、新潟 商談同行：月平均１回程度（１社あたり） 出張：担当顧客による