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 Analytics Specialist plays a critical role in delivering business-partnered insights across VM business, the most commercially significant area within Coca-Cola Bottlers Japan Inc. (CCBJI). This role partners directly with VM teams to provide deep, data-driven recommendations that enable growth in NSR, profitability, and operational efficiency.Beyond analytics delivery, this role leads the design and execution of A/B tests and other hypothesis-driven pilots (e.g., pricing, assortment, or placement), ensuring insights are statistically robust and directly applicable to business strategy. This role also supports the implementation of continuous improvement processes, both in terms of analytics output and data quality.Additionally, the role plays an important role in cross-functional collaboration with the Data Science & AI team, ensuring that fast insights and business-grounded learnings influence AI/ML model development and real-world adoption. This role is expected to foster a data-driven culture within VM, empowering teams to make informed and measurable decisions.Key Responsibilities:Business-Partnered Insights DeliveryDeliver actionable insights to key VM business units, addressing commercial challenges such as declining NSR, margin erosion, suboptimal assortment, or operational inefficiencies.Become a trusted analytics partner for VM stakeholders, providing high-quality recommendations that influence pricing, placement, and sales strategies.Deliver scenario modeling (“what if” analysis) and forecasting to support key business decisions and future-planning.Contribute to business case development for strategic projects (e.g., expansion of pilots or commercial programs).Commercial Deep Dives & Opportunity IdentificationAnalyze trends and drivers of machine-level profitability, product mix effectiveness, workforce allocation, and pricing elasticity.Conduct root cause analyses and variance investigations, providing insights and clear recommendations to address commercial performance gaps.Identify top-line growth and bottom-line efficiency opportunities across VM channels and customer segments.A/B Testing & Pilot ExecutionDesign, implement, and evaluate A/B tests and pilot programs (e.g., pricing, assortment, promotional changes) to support Marketing and Commercial teams.Apply hypothesis testing and advanced statistical methods to ensure experiment results are accurate and actionable.Develop business-ready recommendations based on experimental results, including next steps for scaling or refining initiatives.Support knowledge sharing of experimentation best practices across the VM organization.Collaboration with DS & AI TeamCollaborate with the DS team to ensure business insights inform and enrich DS/AI model development (e.g., Assortment optimization, Column Reallocation).Assist DS teams by validating model results against real-world operational insights and business assumptions.Participate in sprint-based delivery cycles to contribute insights into AI/ML model design and deployment.Agile Delivery & Continuous ImprovementWork within agile delivery frameworks alongside the Analytics Portfolio Manager to ensure timely execution of prioritized analytics tasks.Drive continuous improvement through the automation of recurring analyses and optimization of reporting processes.Contribute to retrospectives and sprint planning, ensuring feedback loops from business teams are incorporated into analytics delivery.Data Quality Monitoring & EnhancementMonitor and identify data quality issues impacting analytics outputs (e.g., missing data, inconsistent definitions, reporting anomalies).Collaborate with ML Data Engineers and Data Governance teams to improve data pipelines and ensure data assets are accurate, timely, and reliable.Implement validation and QA steps within analysis workflows to ensure robust and trusted insights are delivered to VM stakeholders.Key Outputs:High-quality analytics outputs and insights that improve profitability and operational KPIs (e.g., machine-level performance, NSR uplift).Dashboards and reports that drive business decisions in areas such as pricing, assortment, sales, and operations.Completed and validated A/B test and pilot evaluations, with statistically sound recommendations for business scaling or iteration.Operational insights that directly contribute to the refinement and impact of DS/AI models.Documentation of data quality issues and actions taken to remediate them, resulting in improved reliability of analytics deliverables.Performance Success Criteria (Examples):Deliver and actioning insights resulting in X% improvement to NSR or margin uplift within first year.Execute at least 2 statistically robust A/B tests or pilot evaluations with clear business recommendations within the first year.Identify and remediate at least 3 recurring data quality issues, improving the accuracy and reliability of insights delivered to VM stakeholders.Achieve stakeholder satisfaction score of 8/10 or higher within the first 12 months.Reduce turnaround time for recurring analytics deliverables by 15% through process optimization or automation.ポジション概要Lead Analytics Specialistは、コカ・コーラ ボトラーズジャパン（CCBJI）の中核事業である自動販売機（VM）ビジネスにおいて、データを活用した意思決定をリードするポジションです。VM営業・マーケティング・オペレーションなどのチームと密接に連携し、売上（NSR）・収益性・業務効率の向上に貢献する実践的なインサイトを提供します。 また、価格・アソートメント・配置などに関するA/Bテストやパイロット施策の設計・評価を通じて、ビジネス成長を支援します。主な業務内容・　VMビジネスにおける課題（売上、マージン、商品構成、業務効率等）に対する分析・インサイト提供 ・　価格・アソートメント・プロモーションなどのA/Bテストやパイロット施策の企画・評価 ・　シナリオ分析・予測分析を活用した意思決定支援 ・　Power BI等を用いたダッシュボード・レポート作成 ・　Data Science / AIチームと連携した、分析結果のモデル活用・実装支援 ・　データ品質の確認・改善に向けた関係部門との協業このポジションの魅力・　日本最大級の自販機事業において、意思決定に直結する分析に携われる ・　ビジネスに近い立場で、分析結果が実際の施策に反映される手応え ・　Data Science・AI活用を含む、先進的なデータ活用に関与可能 ・　自らの分析で成果創出をリードできる裁量の大きさ

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＞在宅勤務：週2～3日程度 営業同行：月次商談・店舗改装の打ち合わせなどで週1～2回程度 出張：担当顧客に応じて月1回程度（中部・関西エリア）

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首都圏課_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＞在宅勤務：週2～3日程度 営業同行：月次商談・店舗改装の打ち合わせなどで週1～2回程度